1,751 research outputs found

    SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae

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    This paper presents SAMSON, a Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae within a water sample. Designed to be portable and low-cost for on-site use, the optical sub-system of SAMSON consists of a mixture of low-cost optics and electronics, designed specifically to capture both fluorescent and absorption responses from a water sample. The graphical user interface (GUI) sub-system of SAMSON was designed to enable flexible visualisation of algae in the water sample in real-time, with the ability to perform fine-grained exposure control and illumination wavelength selection. We demonstrate SAMSON's capabilities by equipping the system with two fluorescent illumination sources and seven absorption illumination sources to enable the capture of multispectral data from six different algae species (three from the Cyanophyta phylum (blue-green algae) and three from the Chlorophyta phylum (green algae)). The key benefit of SAMSON is the ability to perform rapid acquisition of fluorescence and absorption data at different wavelengths and magnification levels, thus opening the door for machine learning methods to automatically identify and enumerate different algae in water samples using this rich wealth of data

    PORIFERAL VISION: Deep Transfer Learning-based Sponge Spicules Identification & Taxonomic Classification

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    The phylum Porifera includes the aquatic organisms known as sponges. Sponges are classified into four classes: Calcarea, Hexactinellida, Demospongiae, and Homoscleromorpha. Within Demospongiae and Hexactinellida, sponges’ skeletons are needle-like spicules made of silica. With a wide variety of shapes and sizes, these siliceous spicules’ morphology plays a pivotal role in assessing and understanding sponges\u27 taxonomic diversity and evolution. In marine ecosystems, when sponges die their bodies disintegrate over time, but their spicules remain in the sediments as fossilized records that bear ample taxonomic information to reconstruct the evolution of sponge communities and sponge phylogeny. Traditional methods of identifying spicules from core samples of marine sediments are labor-intensive and cannot scale to the scope needed for large analysis. Through the incorporation of high-throughput microscopy and deep learning, image classification has made significant strides toward automating the task of species recognition and taxonomic classification. Even with sparse training data and highly specific image domains, deep convolutional neural networks (DCNNs) were able to extract taxonomic features among morphologically diverse microfossils. Using transfer learning, training a classifier on pretrained DCNNs has achieved recent successes in classifying similar microfossils, such as diatom frustules and radiolarian skeletons. In this project, I address the reliability of pretrained models to perform spicule identification and class-level classification. Using FlowCam technology to photograph individual microparticles, our dataset consists of spicule and non-spicule types without additional image segmentation and augmentation. Our proposed method is a pre-trained model with a custom classifier that performs two different binary classifications: a spicule vs non-spicule classification, and a taxonomic classification of Demospongiae vs. Hexactinellida. We evaluate the effect of implementing different DCNN architectures, data set sizes, and classifiers on image classification performance. Surprisingly, MobileNet, a relatively new and small architecture, showed the best performance while still being the most computationally efficient. Other studies that didn’t involve MobileNet had similar high accuracies for multi-class classifications with fewer training images. The reliability of DCNNs for binary spicule classification implicates the promising approach of a more nuanced multi-class/taxonomic classification. Future work should build multi-class classification that ranges more biogenic materials for the identification or more sponge taxonomic levels for species classification

    A systematic review of deep learning microalgae classification and detection

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    Algae represent the majority of the diversity on Earth and are a large group of organisms that have photosynthetic properties that are important to life. The species of algae are estimated to be more than 1 million, they play an important role in many fields such as agriculture, industry, food, and medicine. It is important to determine the type of algae, to determine if it is harmful or useful, and to indicate the health of the ecosystem, water quality, health, and safety risks. The conventional process of classifying algae is difficult, tedious, and time-consuming. Recently various computer vision techniques have been used to classify algae to overcome challenges and automate the process of classification. This paper presents a review of research done on image classification for microorganism algae using machine learning and deep learning techniques. The paper focuses on three important research questions to highlight the challenges of classifying microalgae. A systematic literature review or SLR has been conducted to determine how deep learning and machine learning have improved and enhanced automatic microalgae classification rather than manual classification. 51 articles have been included from well-known databases. The outcome of this SLR is beneficial due to the detailed analysis and comprehensive overview of the algorithms and the architectures and information about the dataset used in each included article. The future work focuses on getting a large dataset with high resolution, trying different methods to manage imbalance problems, and giving more attention to the fusion of deep learning techniques and traditional machine learning techniques

    SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae

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    This paper presents SAMSON, a Spectral Absorption-fluorescenceMicroscopy System for ON-site-imaging of algae within a watersample. Designed to be portable and low-cost for on-site use,the optical sub-system of SAMSON consists of a mixture of low-cost optics and electronics, designed specifically to capture bothfluorescent and absorption responses from a water sample. Thegraphical user interface (GUI) sub-system of SAMSON was de-signed to enable flexible visualisation of algae in the water samplein real-time, with the ability to perform fine-grained exposure con-trol and illumination wavelength selection. We demonstrate SAM-SON’s capabilities by equipping the system with two fluorescentillumination sources and seven absorption illumination sources toenable the capture of multispectral data from six different algaespecies (three from the Cyanophyta phylum (blue-green algae) andthree from the Chlorophyta phylum (green algae)). The key benefitof SAMSON is the ability to perform rapid acquisition of fluores-cence and absorption data at different wavelengths and magnifica-tion levels, thus opening the door for machine learning methods toautomatically identify and enumerate different algae in water sam-ples using this rich wealth of data

    Optical methods for ultrafast screening of microorganisms

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    En aquesta tesi doctoral hem desenvolupat un mètode per la detecció i quantificació múltiple dels microorganismes més comuns que causen infeccions bacterianes amb una velocitat de detecció sense precedents a baix cost i alta sensibilitat. A més a més, fent servir fluids humans reals directament evitant així, els pretractaments tediosos de les mostres. El disseny del sistema està basat en augments d'intensitat del senyal obtingut per espectroscòpia SERS. Això s'aconsegueix utilitzant nanopartícules plasmòniques codificades i funcionalitzades amb elements de reconeixement biològics. D'aquesta manera, quan una mostra conté el patogen a identificar interactua amb els elements de reconeixement units a les nanopartícules, induint la seva acumulació en la superfície del microorganisme. Aquesta agregació de partícules a la membrana dels bacteris produeix espais molt petits entre les partícules fent que el senyal Raman s'amplifiqui en diversos ordres de magnitud respecte a les partícules soltes. Permetent així, la identificació de múltiples microorganismes a la vegada. La quantificació d'aquests, s'aconsegueix passant la mostra a través d'un dispositiu de micro-fluids amb una finestra de recol•lecció on un làser interroga i classifica els agregats a temps real. A més a més, també hem investigat els avantatges de fer servir aptàmers en lloc d'anticossos com a elements de reconeixement biològic. Aquest nou sistema de detecció de patògens obre interessants perspectives per al diagnòstic ràpid i econòmic d'infeccions bacterianes.En esta tesis doctoral hemos desarrollado un método para la detección y cuantificación múltiple de los microorganismos más comunes que causan infecciones bacterianas con una velocidad de detección sin precedentes a bajo coste y alta sensibilidad. Utilizando además, fluidos humanos reales directamente evitando así, pre-tratamientos tediosos de las muestras. El diseño del sistema está basado en aumentos de intensidad de la señal obtenida por espectroscopia SERS. Esto se logra utilizando nanopartículas plasmónicas codificadas y funcionalizadas con elementos de reconocimiento biológico. De esta manera, cuando una muestra que contiene el patógeno a identificar interactúa con los elementos de reconocimiento unidos a las nanopartículas, induce su acumulación en la superficie del microorganismo. Esta agregación de partículas en las membranas de las bacterias produce espaciados muy pequeños entre las partículas haciendo que la señal Raman se amplifique en varios órdenes de magnitud con respecto a las partículas sueltas. Permitiendo así la identificación de múltiples microorganismos a la vez. La cuantificación de los mismos, se logra pasando la muestra a través de un dispositivo de micro-fluidos con una ventana de recolección donde un láser interroga y clasifica los agregados en tiempo real. Además, también hemos investigado las ventajas de usar aptámeros frente a anticuerpos como elementos de reconocimiento biológico. Este nuevo sistema de detección de patógenos abre interesantes perspectivas para el diagnóstico rápido y barato de las infecciones bacterianas.This doctoral thesis intended to develop and optimize a method for multiplex detection and quantification of the most common microorganisms causing bacterial infections. This detection approach envisions to directly use different real human fluids avoiding thus, tedious pre-treatments of the samples with an unprecedented speed, low cost, and sensitivity. The design of the system is based on variations in the SERS intensity. This is accomplished using encoded plasmonic nanoparticles functionalized with bio-recognition elements. Consequently, when a sample containing the biological target to be identified interacts with the recognition elements attached to the nanoparticle, will induce an accumulation of them at the surface of the targeted microorganism. This particle aggregation on the bacteria membranes renders a dense array of inter-particle gaps in which the Raman signal is amplified by several orders of magnitude relative to the dispersed particles, enabling a multiplexed deterministic identification of the microorganisms. Quantification is achieved by passing the sample through a microfluidic device with a collection window where a laser interrogates and classifies the bacteria–nanoparticle aggregates in real time. Additionally, a comparison between two of the most common bio-recognition elements (antibodies and aptamers) was performed. This new pathogen detection system opens exciting prospects for fast inexpensive diagnosis of bacterial infections
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