Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network

AbstractThe medical practitioners analyze the electrical activity of the human heart so as to predict various ailments by studying the data collected from the Electrocardiogram (ECG). A Bundle Branch Block (BBB) is a type of heart disease which occurs when there is an obstruction along the pathway of an electrical impulse. This abnormality makes the heart beat irregular as there is an obstruction in the branches of heart, this results in pulses to travel slower than the usual. Our current study involved is to diagnose this heart problem using Adaptive Bacterial Foraging Optimization (ABFO) Algorithm. The Data collected from MIT/BIH arrhythmia BBB database applied to an ABFO Algorithm for obtaining best(important) feature from each ECG beat. These features later fed to Levenberg Marquardt Neural Network (LMNN) based classifier. The results show the proposed classification using ABFO is better than some recent algorithms reported in the literature

Metabolite profiling of the chemosphere of the macroalga ulva (ulvales, chlorophyta) and its associated bacteria

The eukaryotic green marine algae Ulva spp. (Chlorophyta), are usually associated with marine bacteria and exhibit therefore microbe-dependent growth and morphotypes. Ulva spp. might actively affect their microbiome by releasing specific compounds in its chemosphere. For instance, algal oxylipins including polyunsaturated aldehydes (PUAs) derived from polyunsaturated acids (PUFAs) might play an important structuring role for the microbiome. In the present study, Ulva spp. collected at various sampling sites in the lagoon of the Ria Formosa (Portugal) have been studied with respect to (1) their ability to produce polyunsaturated aldehydes and (2) their ability to communicate with their surrounding bacteria via infochemicals. Lipoxygenase/hydroperoxidelyase mediated transformations convert polyunsaturated fatty acids into various oxylipins. These fatty acid transformations are highly diverse in marine algae and play a crucial role in e.g., signaling, chemical defense, and stress response often mediated through polyunsaturated aldehydes (PUAs). In this study, Ulva spp. were surveyed for PUAs. Ulva species with sea-lettuce like morphotype were demonstrated to produce elevated amounts of volatile C10-polyunsaturated aldehydes (2,4,7-decatrienal and 2,4-decadienal) upon tissue damage in contrast to Ulva species with tube-like morphotype. Moreover, morphogenetic and phylogenetic analyses of the collected Ulva species revealed chemotaxonomic significance of the perspective biosynthetic pathways. The aldehydes are derived from omega-3 and omega-6 polyunsaturated fatty acids (PUFA) with 20 or 18 carbon atoms including eicosapentaenoic acid (C20:5 n-3), arachidonic acid (C20:4 n-6), stearidonic acid (C18:4 n-3), and gamma-linolenic acid (C18:3 n-6). As first evidences in this study, it was found that lipoxygenase-mediated (11-LOX and 9-LOX) eicosanoid and octadecanoid pathways catalyze the transformation of C20- and C18-polyunsaturated fatty acids into PUAs and concomitantly into short chain hydroxylated fatty acids. Ulva mutabilis Føyn (sl) with tube-like morphotype was used as an objective to investigate the chemical mediated interaction (infochemicals) within the chemosphere of tripartite community consisting of U. mutabilis and its associated marine bacteria i.e., Roseobacter and Cytophaga species. In the absence of these bacteria (axenic conditions), U. mutabilis forms callus-like colonies. However, the combination of the two bacterial strains, Roseobacter sp. and Cytophaga sp. can completely restore the morphogenesis of U. mutabilis forming a symbiotic tripartite community. The exo-metabolome of the chemosphere of this tripartite community was surveyed along with the biological metadata. Two different approaches and cultivation conditions i.e., sterile 25 L bioreactor cultures and non-sterile 200 L outdoor aquacultures were conducted which cultures were inoculated with axenic seven days old germlings. Indeed, it was feasible to observe the whole life cycle of the gametophyte under these conditions when the appropriate bacteria were inoculated as well. Hereby, the medium did not need to be changed. Bioassays revealed that U. mutabilis passed through three statuses of gametogenesis inducibility which can be distinguished whether Ulva is able to onset the gametogenesis: (1) gametogenesis is not inducible, (2) gametogenesis can be induced or (3) it starts even spontaneously. The nutrient depletion over the reproductive cycle shows that the utilization rate of nitrate as a limiting growth factor was significantly high during the inducible status, when the macroalgae was growing. The waterborne metabolites were extracted by solid phase extraction. The samples were directly analyzed by ultra-high performance liquid chromatography (UHPLC) and by gas chromatography (after derivatization) coupled with a time-of-flight mass spectrometer (TOF-MS). Interestingly, discriminant analysis proofed that all waterborne metabolites obtained either from GC-MS or LC-MS were corresponding to the inducibility status of gametogenesis of U. mutabilis in both cultivation conditions. Even more interesting, many unknown biomarkers were found to be common in both bioreactor cultures and aquaculture, insuring the high probability of using these biomarkers as indicators to determine the growth phases corresponding to the status of gametogenesis inducibility in U. mutabilis under any cultivation condition in future land based aquacultures. Moreover, the present study revealed remarkable metabolic fingerprints which might due to the adaptation of U. mutabilis to changes in its surrounding environment. For instance, the algal biomarker 2,4,6-tribromophenol was detected in the chemosphere of the tripartite community under sterile cultivation (bioreactor) but not in the well-defined bacterial community under non-sterile cultivation (aquaculture). In summary, the changes of the metabolite profile between the growth phases were significant. Therefore, various statues in algal growth and life cycle can be predicted based on the dynamics of waterborne metabolites. This knowledge will be essential in order to maintain land based aquacultures providing economical relevant amounts of biomasses

Proteome characterizations of microbial systems using MS-based experimental and informatics approaches to examine key metabolic pathways, proteins of unknown function, and phenotypic adaptation

Microbes express complex phenotypes and coordinate activities to build microbial communities. Recent work has focused on understanding the ability of microbial systems to efficiently utilize cellulosic biomass to produce bioenergy-related products. In order to maximize the yield of these bioenergy-related products from a microbial system, it is necessary to understand the molecular mechanisms.The ability of mass spectrometry to precisely identify thousands of proteins from a bacterial source has established mass spectrometry-based proteomics as an indispensable tool for various biological disciplines. This dissertation developed and optimized various proteomics experimental and informatic protocols, and integrated the resulting data with metabolomics, transcriptomics, and genomics in order to understand the systems biology of bio-energy relevant organisms. Integration of these various omics technologies led to an improved understanding of microbial cell-to-cell communication in response to external stimuli, microbial adaptation during deconstruction of lignocellulosic biomass and proteome diversity when an organism is subjected to different growth conditions.Integrated omics revealed Clostridium thermocellum\u27s accumulate long-chain, branched fatty acids over time in response to cytotoxic inhibitors released during the deconstruction and utilization of switchgrass. A striking feature implies a restructuring of C. thermocellum\u27s cellular membrane as the culture progresses. The membrane remodulation was further examined in a study involving the swarming and swimming phenotypes of Paenibacillus polymyxa. The possible roles of phospholipids, hydrolytic enzymes, surfactin, flagellar assembly, chemotaxis and glycerol metabolism in swarming motility were investigated by integrating lipidomics with proteomics.Extracellular proteome analysis of Caldicellulosiruptor bescii revealed secretome plasticity based on the complexity (mono-/disaccharides vs. polysaccharides) and type of carbon (C5 vs. C6) available to the microorganism. This study further opened the avenue for research to characterize proteins of unknown function (PUFs) specific to growth conditions.To gain a better understanding of the possible functions of PUFs in C. thermocellum, a time course analysis of C. thermocellum was conducted. Based on the concept of guilt-by-association, protein intensities and their co-expressions were used to tease out the functional aspect of PUFs. Clustering trends and network analysis were used to infer potential functions of PUFs. Selected PUFs were further interrogated by the use of phylogeny and structural modeling

Erforschung der Vielfalt und des antimikrobiellen Wirkstoffs räuberischer Bacterien aus indonesischen Mangroven

Mangroves are highly complex coastal ecosystems, which harbor manifold microbial communities including diverse bacterial taxa. The interactions between predatory bacteria such as myxobacteria and mangroves are hitherto poorly understood. Using a polyphasic approach to characterize predatory bacteria from Indonesian mangroves based on culture-dependent and culture-independent approaches and subsequently evaluation of their potential for antimicrobial activity are the aims of our study. We obtained the data on identified cultivable predatory bacteria, 25 isolates of myxobacteria and two other isolates of gliding bacteria which belong to the order Cytophagales, using partial 16S rRNA gene sequences. These 25 myxobacteria isolates were affiliated to six genera: Myxococcus, Corallococcus, Archangium, Chondromyces, Racemicystis, and Nannocystis of the order Myxococcales. However, the culture-independent approach showed that myxobacteria communities are more diverse than assumed. The polyphasic approach led to the identification of a new strain, Ohtaekwangia 313MSO from Indonesian mangrove at Jakarta. The evaluation of six housekeeping genes for multilocus sequences analysis (MLSA) of Myxococcus spp. isolates revealed genetically distinct six Myxococcus strains. Thirteen crude extracts showed moderate activities against at least one human pathogenic microorganism and the crude extract of Racemicystis persica strain 503MSO indicated a novel compound, which has not been reported in the myxobase database yet. Therefore, identification of this compound is needed for further study.Mangroven sind hochkomplexe küstennahe Ökosysteme, die vielfältige mikrobielle Gemeinschaften, einschließlich verschiedener bakterieller Gruppen, beherbergen. Die Wechselwirkungen zwischen räuberischen Bakterien wie Myxobakterien und Mangroven sind bisher kaum bekannt. Die Durchführung eines mehrphasigen Ansatzes zur Charakterisierung räuberischer Bakterien aus indonesischen Mangroven auf der Grundlage kulturabhängiger und kulturunabhängiger Ansätze und die anschließende Bewertung ihres Potenzials hinsichtlich der antimikrobiellen Aktivität sind die Ziele dieser Studie. Wir präsentieren Daten zu identifizierten kultivierbaren räuberischen Bakterien von 25 Isolaten der Myxobakterien und zwei weiteren Isolaten von gleitenden Bakterien, die zur Ordnung Cytophagales gehören, unter Verwendung partieller 16S-rRNA-Gensequenzen. Diese 25 Myxobakterien-Isolate wurden sechs Gattungen zugeordnet: Myxococcus, Corallococcus, Archangium, Chondromyces, Racemicystis und Nannocystis aus der Ordnung der Myxococcales. Ein kulturunabhängiger Ansatz zeigte jedoch, dass Myxobakteriengemeinschaften vielfältiger sind als angenommen. Der mehrphasige Ansatz führte zur Isolierung eines neuen Stammes, des Stammes Ohtaekwangia 313MSO, aus Jakarta, genauer aus den indonesischen Mangroven. Die Bewertung von sechs Housekeeping-Genen für die multilocus-Sequenzanalyse (MLSA) von Myxococcus spp. zeigten sechs genetisch unterschiedliche Myxococcus-Stämme. Dreizehn Rohextrakte zeigten mäßige Aktivitäten gegen mindestens einen der humanpathogenen Mikroorganismen, und der Rohextrakt aus Racemicystis persica Stamm 503MSO enthält eine neue Verbindung, über die in der Myxobase Datenbank noch nicht berichtet wurde. Daher sind zur Identifizierung dieser Verbindung weitere Untersuchungen erforderlich

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Evolving Morphologies for Locomoting Micro-scale Robotic Agents

Designing locomotive mechanisms for micro-scale robotic systems could enable new approaches to tackling problems such as transporting cargos, or self-assembling into pre-programmed architectures. Morphological factors often play a crucial role in determining the behaviour of micro-systems, yet understanding how to design these aspects optimally is a challenge. This paper explores how the morphology of a multi-cellular micro-robotic agent can be optimised for reliable locomotion using artificial evolution in a stochastic environment. We begin by establishing the theoretical mechanisms that would allow for collective locomotion to emerge from contractile actuations in multiple connected cells. These principles are used to develop a Cellular Potts model, in order to explore the locomotive performance of morphologies in simulation. Evolved morphologies yield significantly better performance in terms of the reliability of the travel direction and the distance covered, compared to random morphologies. Finally, we demonstrate that patterns in evolved morphologies are robust to small imperfections and generalise well to larger morphologies

Antimicrobial mechanism of action of 3,4-methylenedioxy-beta-nitropropene

This research investigated the mechanism of action in bacteria of 3,4-methylenedioxy-β-nitropropene (BDM-I), a very broad spectrum antimicrobial lead compound in development as an anti-infective drug. The thesis proposes that BDM-I inhibits bacterial protein tyrosine phosphatases, a novel mechanism of action for an antimicrobial agent and a new target in microorganisms. This very open investigation was directed by considerable biological information on the effects of BDM-I in microorganisms and animals which provided insights into possible and improbable cellular targets. The biological effects of BDM-I were investigated using biochemical and cell-based assays, transmission electron microscopy and whole genome DNA microarray analysis. The specific experiments and order of execution were largely dependent on information gained as the project progressed. BDM-I was shown not to target the metabolic pathways of the major classes of antibacterial drugs, which supports a novel mechanism of action. Investigation of several species-specific effects suggested that cell signalling pathways were a possible target. Based on the structure of BDM-I and review of the scientific literature on cell signalling in bacteria, the hypothesis that BDM-I acted by inhibition of protein tyrosine phosphatases (PTP) was supported by demonstrating inhibition of human and bacterial PTP's in an enzyme assay. This mechanism was consistent with other demonstrated effects: inhibition of the intracellular pathogen, Chlamydia trachomatis; inhibition of swarming in Proteus spp. and inhibition of pigment production in Serratia marcescens; and with kill kinetics in bacteria and yeast. A pilot global genome analysis of BDM-I treated Bacillus subtilis did not detect differential expression of PTP genes but has provided many avenues for further investigation. This research further supports the development of BDM-I as a broad spectrum anti-infective drug

Summer Research Fellowship Project Descriptions 2022

A summary of research done by Smith College’s 2021 Summer Research Fellowship (SURF) Program participants. Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. Supervised by faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants were asked to summarize their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1012/thumbnail.jp

Encoding and decoding information within native and engineered bacterial swarm patterns

Pattern formation, or the generation of coordinated, emergent behavior, is ubiquitous in nature. Researchers have long sought to understand the mechanisms behind such systems as zebra stripes, repeating flower petals, and fingers on hands, within fields such as physics and developmental biology. Notably, a diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella. Meanwhile, researchers in the synthetic biology field, which aims to rationally engineer living organisms for biotechnological applications, have been engineering synthetic pattern formation in microbes over the last several decades. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. In this thesis, we expand the field of engineered pattern formation by applying the tools of synthetic biology and deep learning to engineer and characterize the swarming of Proteus mirabilis, which natively forms a centimeter-scale ring pattern. We engineer P. mirabilis to “write” external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we apply convolutional neural networks (CNNs) to decode the resulting patterns with over 90% top-3 accuracy. We separately show growing colonies can record dynamic environmental changes which can be decoded with a U-Net model. We show the robustness of the engineered strains’ readout to fluctuations in temperature and environmental water samples. Lastly, we engineer strains which sense and respond to heavy metals. Our pCopA-flgM strain records the presence of 0 to 50 mM aqueous copper with decreased colony ring width. We conclude in this chapter that engineering native swarm patterns can thus be applied for building bacterial recorders with a visible macroscale readout. In parallel, to better characterize the swarm patterns of P. mirabilis, we develop a pipeline using deep learning approaches to segment colony images. We develop easy-to-use, semi-automated ground truth annotation and preprocessing methods. We separately segment the (1) colony background from agar and (2) the internal colony ring boundaries. The first task is achieved with a patch-classification approach; in the process, we find that the combination of the trained CNN and the “majority voting” method of label fusion achieves a test DICE score of 93% and correctly segments even faint outer swarm rings. The second task is accomplished with a U-Net which achieves over 83% test DICE. We show that our trained models easily segment a set of colonies generated at two relevant conditions, enabling automated analysis of features such as area and ring width. We apply our pipeline to analyze the more complex patterns of our engineered strains, such as the pCopA-flgM strain. The work in this chapter altogether advances the ability to analyze swarm patterns of P. mirabilis. We also aim to expand the use of our colony-characterization approaches beyond P. mirabilis to other microbes. Therefore, we present our work using deep learning to classify a set of Bacillus species isolated from soil samples. We generate datasets of the species grown under different conditions and apply transfer learning to train well-known CNN architectures such as ResNet and Inception to classify these datasets. This approach allows the models to easily learn these small datasets, and the models generalize to correctly predict a species which forms branching patterns regardless of exact growth condition. We visualize the attributions of the models with the integrated gradients method and find that model predictions are attributable to colony regions. This work sets the stage for classification, segmentation, and characterization of a wider array of microbial species with distinctive macroscale colony morphologies. Finally, we conclude by discussing ongoing efforts to expand upon the work presented in this thesis towards the sensing of dynamic inputs such as light, engineering of species other than P. mirabilis, and further optimization of the system of an engineered swarm pattern as a macroscale biosensor readout. Such work can contribute not only to the fields of synthetic pattern formation and the study of bacterial swarming, but also to the fields of engineered living materials and bio-inspired design