141 research outputs found

    Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

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    Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method

    Use of geospatial techniques to improve bee farming and bee health across four main agroecological zones in Kenya.

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    Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Amid augmented climate change and anthropogenic influence on natural environments and agricultural systems, the global socioeconomic and environmental value of bees is undisputed. Bee products such as honey, pollen, nectar, royal jelly and to a lesser extent bee venom are important supplemental sources of income generation especially in the underdeveloped rural African areas. Moreover, bee farming is an important incentive for forest conservation, biodiversity and ecosystem services in terms of pollination services. Bee pollination services play a vital role in crop production, hence directly contribute to food and nutritional security for African smallholder farmers. Nevertheless, bee farming and bee health in general are under threat from climate change, agricultural intensification and associated habitat alteration, agrochemicals intensification, bee pests and diseases. Therefore, there is need to establish spatial distribution of bees, their food substrates, floral cycle and biotic and abiotic threats, especially bee pests. Bee pests devastate bee colonies through physical injury and as vectors of pathogens, hence causing a considerable reduction in bee colony productivity. Thus, this study sought to establish geospatial techniques that could be used to improve bee farming and bee health in Kenya. Firstly, this study aimed to determine the spatial and temporal distribution of stingless bees in Kenya using six machine learning ecological niche approaches and non-conflating variables from both bioclimatic, vegetation phenology and topographic features. All machine learning algorithms used herein performed at an ‘excellent’ level with a true skills statistics (TSS) score of up to 0.91. Secondly, the study assessed the suitability of resampled multispectral data for mapping melliferous (flowering plants that produce substance used by bees to produce honey) plants in Kenya. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors’ (i.e., WorldView-2, RapidEye, Spot- 6 and Sentinel-2) spatial and spectral resolutions, and a RF classifier were used to map melliferous plants. Melliferous plants were successfully mapped with up to 93.33% overall accuracy using WorldView-2. Furthermore, the study predicted the distribution of four main bee pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor) in Kenya using the maximum entropy (MaxEnt) model and random forest (RF) classifier. The effect of seasonality on the abundance of bee pests was apparent, as indicated by the Wilcoxon rank sum test, with up to 6.35 times more pests in the wet than the dry season. Furthermore, bioclimatic variables especially precipitation contributed the most (up to 77.8%) to all bee pest predictions, while vegetation phenology provided vital information needed to sharpen the prediction models at grain level due to their higher spatial resolution and seasonal and phenological features. Moreover, topography had a moderate influence (14.3%) on the distribution of bee pests. Also, there was a positive correlation between bee pests’ abundance, habitat suitability and high altitude. Anthropogenic influence (as depicted by human footprint data) on the distribution of bee pests was relatively low (1.2%) due to the availability of a variety of bee food substrate from the mixed land use/land cover (LULC) classes, especially farmlands. Using the Pearson correlation coefficient, the prediction models for all bee pests scored at an excellent level (0.84), except for the G. mellonella prediction model, which was ranked ‘fair’ (0.55). Due to the relatively high accuracy for models developed herein to map stingless bees’ distribution, melliferous plants and bee pests’ occurrence and abundance, this study concluded that the models developed could reliably be used to indicate high suitability areas for bee farming. They could also be used to predict high bee pests risk areas for mitigation and management purposes, hence improving bee health and hive productivity

    Fluorescence Multiplexing with Combination Probes for Biological and Diagnostic Applications

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    Cancer refers to a group of diseases containing more than 200 different subtypes. Cancer is a heterogeneous disease by nature, meaning that there are differences among tumors of the same type in different patients, and there are differences among cancer cells within a single tumor of one patient. Since cancer is not a single disease, nor does it have a single cause, it proves to be incredibly hard to diagnose and treat. The ability to study cellular markers, cell and tissue spatial arrangement, and gene function are all integral parts of cancer diagnostic and treatment efforts. Here, I first present a review of current techniques for quantitative tissue imaging at cellular resolution. I broadly divide current imaging techniques into three categories: fluorescence-based, mass spectrometry-based, and sequencing-based. In this work, I primarily concentrate on fluorescence-based methods, with the focus being on our recently developed theory Multiplexing using Spectral Imaging and Combinatorics (MuSIC). The basis for MuSIC is to create combinations of fluorescent molecules (whether it be small molecule fluorophores or fluorescent proteins) to create unique spectral signatures. I then present a protocol for labeling antibodies with combinations of small molecule fluorophores, which I refer to as MuSIC probes. I use fluorescent oligonucleotides (oligos) to arrange the fluorophores at specified distances and orientations from one another in order to produce complex fluorescence spectra when the probe is excited. This labeling protocol is demonstrated using a 3-probe experimental setup, bound to Protein A beads, and analyzed via spectral flow cytometry. When translating this method to staining human cells, our staining intensity was not comparable to that of a conventional antibody labeling kit. Therefore, next I present an improved method to label antibodies with MuSIC probes with increased signal intensity. I re-arrange the oligo-fluorophore arrangement of the MuSIC probe to emit an increased fluorescent signal. Then I validate this approach by comparing the staining intensity of MuSIC probe-labeled antibodies to a conventional antibody labeling kit using human peripheral blood mononuclear cells. Lastly, I present simulation theories for the multiplexing capabilities of MuSIC probes for various biological and diagnostic applications. First, I present a theory for high-throughput genetic interaction screening using MuSIC probes generated from 18 currently available fluorescent proteins. Simulation studies based on constraints of current spectral flow cytometry equipment suggest our ability to perform genetic interaction screens at the human genome-scale. Finally, I adapt this simulation protocol to generate MuSIC probes from 30 currently available small-molecule fluorophores. Using the same constraints as before, I predict that I can perform cell-type profiling of 200+ analytes. I hope that the work presented here provides a foundation for the use of combination probes for various biological and disease applications and ultimately help to better diagnose and treat different types of cancer

    Hyperspectral drill-core scanning in geometallurgy

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    Driven by the need to use mineral resources more sustainably, and the increasing complexity of ore deposits still available for commercial exploitation, the acquisition of quantitative data on mineralogy and microfabric has become an important need in the execution of exploration and geometallurgical test programmes. Hyperspectral drill-core scanning has the potential to be an excellent tool for providing such data in a fast, non- destructive and reproducible manner. However, there is a distinct lack of integrated methodologies to make use of these data through-out the exploration and mining chain. This thesis presents a first framework for the use of hyperspectral drill-core scanning as a pillar in exploration and geometallurgical programmes. This is achieved through the development of methods for (1) the automated mapping of alteration minerals and assemblages, (2) the extraction of quantitative mineralogical data with high resolution over the drill-cores, (3) the evaluation of the suitability of hyperspectral sensors for the pre-concentration of ores and (4) the use of hyperspectral drill- core imaging as a basis for geometallurgical domain definition and the population of these domains with mineralogical and microfabric information.:Introduction Materials and methods Assessment of alteration mineralogy and vein types using hyperspectral data Hyperspectral imaging for quasi-quantitative mineralogical studies Hyperspectral sensors for ore beneficiation 3D integration of hyperspectral data for deposit modelling Concluding remarks Reference

    Mechanisms and Consequences of Microtubule-Based Symmetry Breaking in Plant Roots

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    Directional growth in plants is primarily determined by the axis of cell expansion, which is specified by the net orientation of cortical microtubules. Microtubules guide the deposition of cellulose and other cell wall materials. In rapidly elongating cells, transversely oriented microtubules create material anisotropy in the cell wall that prevents radial cell expansion, channeling cell expansion in the longitudinal direction. Mutations perturbing microtubule organization frequently lead to aberrant cell growth in land plants, with some mutations leading to helical growth patterns (called ‘twisted mutants’), often in roots. This phenotype manifests as right-handed or left-handed twisting of cell files along the long axis of plant organs, which correlates with rightward or leftward organ growth, respectively. Helical growth is a common occurrence in the plant kingdom and serves a variety of purposes, but the molecular mechanisms that produce helical growth and define handedness are not well understood. Furthermore, how molecular-level processes propagate across spatial scales to control organ-level growth is undefined. Here, I used the model plant Arabidopsis thaliana as an experimentally tractable system, focusing on the root organ to study the mechanisms underlying helical growth in plants. In this work, I used roots as a model plant organ to investigate the molecular mechanisms that control symmetry maintenance and symmetry breaking in plants. Arabidopsis roots are ideally suited for this work because of their simple, concentric ring-like cellular anatomy and well-defined process of development. I selected two Arabidopsis twisted mutants with opposite chirality to study whether the emergence of right-handed and left-handed helical growth involves conserved or distinct mechanisms. Cortical microtubules are skewed in the right-handed spr1 mutant, which lacks a microtubule plus end-associated protein that regulates polymerization dynamics. In contrast, cortical microtubules tend to be laterally displaced in the left-handed cmu1 mutant, which lacks a protein that contributes to the attachment of cortical microtubules to the plasma membrane. Using a cell-type specific complementation approach, I showed that both SPR1 and CMU1 gene expression in the epidermis alone is sufficient to maintain wild-type-like straight cell files and root growth. In addition, epidermal expression of SPR1 restores both the morphology and skew of the cortical cell file to wild-type-like. By genetically disrupting cell-cell adhesion in the spr1 mutant, I found that a physical connection between epidermal and cortical cells is required for the epidermis to cause organ-level skewed growth. Together, these data demonstrate that the epidermis plays a central role in maintaining straight root growth, suggesting that twisted plant growth in nature could arise by altering microtubule behavior in the epidermis alone and does not require null alleles in all cells. To examine whether cortical microtubule defects in the spr1-3 mutant affect cell growth, I conducted morphometry analysis. I found that while skewed cortical microtubule orientation correlates with asymmetric epidermal cell morphology and growth in the spr1-3 mutant root meristem, cell file twisting is not manifested until the differentiation zone of the root where cell growth slows down and root hairs emerge. Furthermore, I demonstrated that cell file twisting is not sufficient to generate skewed growth at the organ level, which requires that the root is grown on an agar medium, a mechanically heterogeneous environment. Increasing the stiffness of the agar medium caused the spr1-3 and cmu1 mutant roots to grow straight, indicating that mechanical stimuli influence twisted root growth. Despite their important role in root anchorage, root hairs on the epidermis are not required for skewed root growth, nor for reorienting root skewing in response to changes in the mechanical environment. Overall, this work provides new insights into how symmetry breaking affects root mechanoresponse. Spatial heterogeneity in the composition and organization of the plant cell wall affects its mechanics to control cell shape and directional growth. In the last chapter of this work, I describe a new methodology for imaging plant primary cell walls at the nanoscale using atomic force microscopy coupled with infrared spectroscopy (AFM-IR). I contributed to generating a novel sample preparation technique and employed AFM-IR and spectral deconvolution to generate high-resolution spatial maps of the mechanochemical signatures of the Arabidopsis epidermal cell wall. Cross-correlation analysis of the spatial distribution of chemical and mechanical properties suggested that the carbohydrate composition of cell wall junctions correlates with increased local stiffness. In developing this methodology, this chapter provides an essential foundation for applying AFM-IR to understand the complex mechanochemistry of intact plant cell walls at nanometer resolution

    Uncertainty Quantification in Biophotonic Imaging using Invertible Neural Networks

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    Owing to high stakes in the field of healthcare, medical machine learning (ML) applications have to adhere to strict safety standards. In particular, their performance needs to be robust toward volatile clinical inputs. The aim of the work presented in this thesis was to develop a framework for uncertainty handling in medical ML applications as a way to increase their robustness and trustworthiness. In particular, it addresses three root causes for lack of robustness that can be deemed central to the successful clinical translation of ML methods: First, many tasks in medical imaging can be phrased in the language of inverse problems. Most common ML methods aimed at solving such inverse problems implicitly assume that they are well-posed, especially that the problem has a unique solution. However, the solution might be ambiguous. In this thesis, we introduce a data-driven method for analyzing the well-posedness of inverse problems. In addition, we propose a framework to validate the suggested method in a problem-aware manner. Second, simulation is an important tool for the development of medical ML systems due to small in vivo data sets and/or a lack of annotated references (e. g. spatially resolved blood oxygenation (sO 2 )). However, simulation introduces a new uncertainty to the ML pipeline as ML performance guarantees generally rely on the testing data being sufficiently similar to the training data. This thesis addresses the uncertainty by quantifying the domain gap between training and testing data via an out-of-distribution (OoD) detection approach. Third, we introduce a new paradigm for medical ML based on personalized models. In a data-scarce regime with high inter-patient variability, classical ML models cannot be assumed to generalize well to new patients. To overcome this problem, we propose to train ML models on a per-patient basis. This approach circumvents the inter-patient variability, but it requires training without a supervision signal. We address this issue via OoD detection, where the current status quo is encoded as in-distribution (ID) using a personalized ML model. Changes to the status quo are then detected as OoD. While these three facets might seem distinct, the suggested framework provides a unified view of them. The enabling technology is the so-called invertible neural network (INN), which can be used as a flexible and expressive (conditional) density estimator. In this way, they can encode solutions to inverse problems as a probability distribution as well as tackle OoD detection tasks via density-based scores, like the widely applicable information criterion (WAIC). The present work validates our framework on the example of biophotonic imaging. Biophotonic imaging promises the estimation of tissue parameters such as sO 2 in a non-invasive way by evaluating the “fingerprint” of the tissue in the light spectrum. We apply our framework to analyze the well-posedness of the tissue parameter estimation problem at varying spectral and spatial resolutions. We find that with sufficient spectral and/or spatial context, the sO 2 estimation problem is well-posed. Furthermore, we examine the realism of simulated biophotonic data using the proposed OoD approach to gauge the generalization capabilities of our ML models to in vivo data. Our analysis shows a considerable remaining domain gap between the in silico and in vivo spectra. Lastly, we validate the personalized ML approach on the example of non-invasive ischemia monitoring in minimally invasive kidney surgery, for which we developed the first-in-human laparoscopic multispectral imaging system. In our study, we find a strong OoD signal between perfused and ischemic kidney spectra. Furthermore, the proposed approach is video-rate capable. In conclusion, we successfully developed a framework for uncertainty handling in medical ML and validated it using a diverse set of medical ML tasks, highlighting the flexibility and potential impact of our approach. The framework opens the door to robust solutions to applications like (recording) device design, quality control for simulation pipelines, and personalized video-rate tissue parameter monitoring. In this way, this thesis facilitates the development of the next generation of trustworthy ML systems in medicine

    Diffuse Optical Imaging with Ultrasound Priors and Deep Learning

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    Diffuse Optical Imaging (DOI) techniques are an ever growing field of research as they are noninvasive, compact, cost-effective and can furnish functional information about human tissues. Among others, they include techniques such as Tomography, which solves an inverse reconstruction problem in a tissue volume, and Mapping which only seeks to find values on a tissue surface. Limitations in reliability and resolution, due to the ill-posedness of the underlying inverse problems, have hindered the clinical uptake of this medical imaging modality. Multimodal imaging and Deep Learning present themselves as two promising solutions to further research in DOI. In relation to the first idea, we implement and assess here a set of methods for SOLUS, a combined Ultrasound (US) and Diffuse Optical Tomography (DOT) probe for breast cancer diagnosis. An ad hoc morphological prior is extracted from US B-mode images and utilised for the regularisation of the inverse problem in DOT. Combination of the latter in reconstruction with a linearised forward model for DOT is assessed on specifically designed dual phantoms. The same reconstruction approach with the incorporation of a spectral model has been assessed on meat phantoms for reconstruction of functional properties. A simulation study with realistic digital phantoms is presented for an assessment of a non-linear model in reconstruction for the quantification of optical properties of breast lesions. A set of machine learning tools is presented for diagnosis breast lesions based on the reconstructed optical properties. A preliminary clinical study with the SOLUS probe is presented. Finally, a specifically designed deep learning architecture for diffusion is applied to mapping on the brain cortex or Diffuse Optical Cortical Mapping (DOCM). An assessment of its performances is presented on simulated and experimental data

    Modeling, Simulation and Data Processing for Additive Manufacturing

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    Additive manufacturing (AM) or, more commonly, 3D printing is one of the fundamental elements of Industry 4.0. and the fourth industrial revolution. It has shown its potential example in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing and local on-demand manufacturing are some of the current benefits. Businesses based on AM have experienced double-digit growth in recent years. Accordingly, we have witnessed considerable efforts in developing processes and materials in terms of speed, costs, and availability. These open up new applications and business case possibilities all the time, which were not previously in existence. Most research has focused on material and AM process development or effort to utilize existing materials and processes for industrial applications. However, improving the understanding and simulation of materials and AM process and understanding the effect of different steps in the AM workflow can increase the performance even more. The best way of benefit of AM is to understand all the steps related to that—from the design and simulation to additive manufacturing and post-processing ending the actual application.The objective of this Special Issue was to provide a forum for researchers and practitioners to exchange their latest achievements and identify critical issues and challenges for future investigations on “Modeling, Simulation and Data Processing for Additive Manufacturing”. The Special Issue consists of 10 original full-length articles on the topic

    Hyperspectral imaging and its application on vaccinium myrtillus leaves

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    Abstract. Hyperspectral imaging is a remote sensing technique which can be used to study different kinds of targets in continuous range of wavelengths in a contiguous manner. In this thesis, hyperspectral imaging was applied in the study of bilberry leaves (Vaccinium myrtillus). The aim of this master’s thesis was to become familiar with the hyperspectral imaging as a data acquisition technique, and to understand the physics behind it. The goal in applying the imaging to the study of bilberry was to be able to distinguish lifeless and healthy bilberry leaves from one another, and consequently, detect signs of plant stress. The hyperspectral camera used in this thesis captured wavelengths ranging from 400nm to 1000nm which covers most of the visible region and part of the near-infrared region of the electromagnetic spectrum. For vegetation, interesting phenomena, like chlorophyll and water absorption, take place in this wavelength range. The bilberry leaves were studied in three experiment setups. First, in a laboratory-like setup, and then in two different field study setups. The inspected parameters in the studies were the water content of the leaves and their ability to absorb chlorophyll. Finally, the gathered data was analysed using multiple methods including PCA and MCR, and the use of vegetation indices
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