6,255 research outputs found

    High-throughput Tools and Techniques to Investigate Environmental Effects on Aging Behaviors in Caenorhabditis elegans

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    Aging is modulated by genetic and environmental cues; however, it is difficult to study how these perturbations modulate the aging process in a robust, high-throughput manner. Methods to gather large-scale behavioral data for aging studies are labor-intensive, lack individual-level resolution, or lack precise spatiotemporal environmental control. In addition, tools to analyze large-scale behavioral data sets are difficult to scale, unable to be broadly applied across complex environments, or fail to detect subtle behavioral changes. In this thesis I develop tools to enable robust, microfluidic culture and behavioral analysis of C. elegans to examine how environmental cues, such as dietary restriction, influence longevity and behavior with age. In Aim 1, I engineer a robust pipeline for the long-term longitudinal culture and behavioral monitoring of C. elegans in aging studies with precise spatiotemporal environmental control. In Aim 2, I develop a flexible deep learning based pipeline for detecting and extracting postural information from large-scale behavioral datasets across heterogeneous environments. In Aim 3, I characterize how the full behavioral repertoire of individuals change with age, along with examining how these age-related behavioral changes are modulated by different dietary restriction regimes. The completion of this thesis provides 1) a new toolset to robustly explore how genetic or environmental effects influence longevity and healthspan, 2) a flexible pipeline for analyzing large-scale behavioral data in C. elegans, and 3) insight into how environmental perturbations influence health through age-related changes in behavior.Ph.D

    Computational Geometry Contributions Applied to Additive Manufacturing

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    This Doctoral Thesis develops novel articulations of Computation Geometry for applications on Additive Manufacturing, as follows: (1) Shape Optimization in Lattice Structures. Implementation and sensitivity analysis of the SIMP (Solid Isotropic Material with Penalization) topology optimization strategy. Implementation of a method to transform density maps, resulting from topology optimization, into surface lattice structures. Procedure to integrate material homogenization and Design of Experiments (DOE) to estimate the stress/strain response of large surface lattice domains. (2) Simulation of Laser Metal Deposition. Finite Element Method implementation of a 2D nonlinear thermal model of the Laser Metal Deposition (LMD) process considering temperaturedependent material properties, phase change and radiation. Finite Element Method implementation of a 2D linear transient thermal model for a metal substrate that is heated by the action of a laser. (3) Process Planning for Laser Metal Deposition. Implementation of a 2.5D path planning method for Laser Metal Deposition. Conceptualization of a workflow for the synthesis of the Reeb Graph for a solid region in ℝ" denoted by its Boundary Representation (B-Rep). Implementation of a voxel-based geometric simulator for LMD process. Conceptualization, implementation, and validation of a tool for the minimization of the material over-deposition at corners in LMD. Implementation of a 3D (non-planar) slicing and path planning method for the LMD-manufacturing of overhanging features in revolute workpieces. The aforementioned contributions have been screened by the international scientific community via Journal and Conference submissions and publications

    Identification of Micro- and Submicron (Nano) Plastics in Water Sources and the Impact of COVID-19 on Plastic Pollution

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    One of the most significant environmental issues that our society may deal with this century could be plastics. The world's water bodies, as well as land and air, are becoming more and more contaminated by plastic due to the ongoing and expanding manufacturing of these synthetic materials, as well as the lack of an effective strategy for managing plastic waste. The fact that plastics break down into smaller particles (micro and nanoplastics) by action of environmental physical and chemical reactions, and do not degrade biologically in a reasonable time, is a cause of concern as plastics are believed to cause harm in animals, plants and humans.To identify the types of plastics prevalent in aquatic habitats, a number of procedures have been developed, from sampling to identification. After a water body has been sampled using nets, pumps, or other tools, depending on the type of sample taken, it is usually necessary to treat the samples for separation and purification. The next stage is to employ analytical techniques to identify the synthetic contaminants. The most common approaches are microscopy, spectroscopy, and thermal analysis. This thesis gives an overview of where in the environment microplastics (MPs) and nanoplastics (NPs) can be found and summarizes the most important technologies applied to analyse the importance of plastics as a contaminant in water bodies. The development of standardised analytical procedures is still necessary as most of them are not suitable for the identification of particles below 50 μm due to resolution limitations. The preparation and analysis of samples are usually time-consuming factors that shall be considered. Particularly for MP and NP analysis in aqueous samples, thermal analysis methods based on sample degradation are generally not considered to be the most effective approach. Nevertheless, Pyrolysis - Gas Chromatography Time-of-Flight Mass Spectrometry (Py-GCToFMS) is used in this thesis to propose a novel approach as due to its unique detection abilities, and with a novel filtration methodology for collection, it enables the identification of tiny particle sizes (>0.1 μm) in water samples.PTFE membranes were selected to filter the liquid samples using a glass filtration system. This way, the synthetic particles will be deposited on the membranes and will allow the study and analysis of the precipitated material. PTFE is a readily available, reasonably priced, and adaptable product that makes sample preparation quick and simple.The three plastics under study—polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC)—can be identified from complex samples at trace levels thanks to the employment of these widely used membranes and the identification of various and specific (marker) ions. The technique was examined against a range of standards samples that contained predetermined concentrations of MPs and NPs. Detection levels were then determined for PVC and PS and were found to be below <50 μg/ L, with repeatable data showing good precision (RSD <20 %). The examination of a complex matrix sample taken from a nearby river contributed to further validate this innovative methodology; the results indicated the existence of PS with a semi-quantifiable result of 250.23 g/L. Because of this, PY-GCToFMS appears to be a method that is appropriate for the task of identifying MPs and NPs from complex mixtures.This thesis also focuses on the environmental challenge that disposable plastic face masks (DPFMs) pose, which has been made significantly worse due to the COVID-19 pandemic. By the time this thesis was written, the production of disposable plastic facemasks had reached to approximately 200 million a day, in a global effort to tackle the spread of the new SARS-CoV-2 virus. This thesis investigates the emissions of pollutants from several different DPFM brands (medical and non-medical) that were submerged in water to replicate the conditions in the environment after these DPFMs have been discarded. The DPFM leachates were filtered using inorganic membranes type and characterized using Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS), Light/Optical Microscopy (LM/OM), Inductively coupled plasma mass spectrometry (ICP-MS) and Liquid chromatography–mass spectrometry (LC-MS). Micro and nano scale polymeric fibres, particles, siliceous fragments and leachable inorganic and organic chemicals were observed from all of the tested DPFMs. For non-medical DPFMs, traces of concerning heavy metals were detected in association with silicon containing fragments (i.e. lead up to 6.79 μg/L). ICP-MS also confirmed the presence of other leachable metals like cadmium (up to 1.92 μg/L), antimony (up to 3.93 μg/L) and copper (up to 4.17 μg/L). LC-MS analysis identified organic species related to plastic additives; polyamide-66 monomer and oligomers (nylon-66 synthesis), surfactant molecules, and dye-like molecules were all tentatively identified in the leachate. The question of whether DPFMs are safe to use daily and what implications may be anticipated after their disposal into the environment is brought up by the toxicity of some of the chemicals discovered.The previous approach is expanded to medical DPFMs with the utilisation of Field Emission Gun Scanning Electron Microscope (FEG-SEM) in order to get high resolution images of the micro and nanoparticles deposited on the membranes. It is also incorporated the use of 0.02 μm pore size inorganic membranes to better identify the nanoparticles released.Separated aqueous samples were also obtained by submerging medical DPFMs for 24 hours to be analysed using ICP-MS and LC-MS.Both particles and fibres in the micro and nano scale were found in all 6 DPFMs brands of this study. EDS analysis revealed the presence of particles containing different heavy metals like lead, mercury, and arsenic among others. ICP-MS analysis results confirmed traces of heavy metals (antimony up to 2.41 μg/L and copper up to 4.68 μg/L). LC-MS analysis results identified organic species related to plastic additives and contaminants; polyamide-66 monomer and oligomers (nylon-66 synthesis), surfactant molecules, and polyethylene glycol were all tentatively identified in the leachate. The toxicity of some of the chemicals found raises the question of whether DPFMs are safe to be used on a daily basis and what consequences are to be expected after their disposal into the environment

    On noise, uncertainty and inference for computational diffusion MRI

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    Diffusion Magnetic Resonance Imaging (dMRI) has revolutionised the way brain microstructure and connectivity can be studied. Despite its unique potential in mapping the whole brain, biophysical properties are inferred from measurements rather than being directly observed. This indirect mapping from noisy data creates challenges and introduces uncertainty in the estimated properties. Hence, dMRI frameworks capable to deal with noise and uncertainty quantification are of great importance and are the topic of this thesis. First, we look into approaches for reducing uncertainty, by de-noising the dMRI signal. Thermal noise can have detrimental effects for modalities where the information resides in the signal attenuation, such as dMRI, that has inherently low-SNR data. We highlight the dual effect of noise, both in increasing variance, but also introducing bias. We then design a framework for evaluating denoising approaches in a principled manner. By setting objective criteria based on what a well-behaved denoising algorithm should offer, we provide a bespoke dataset and a set of evaluations. We demonstrate that common magnitude-based denoising approaches usually reduce noise-related variance from the signal, but do not address the bias effects introduced by the noise floor. Our framework also allows to better characterise scenarios where denoising can be beneficial (e.g. when done in complex domain) and can open new opportunities, such as pushing spatio-temporal resolution boundaries. Subsequently, we look into approaches for mapping uncertainty and design two inference frameworks for dMRI models, one using classical Bayesian methods and another using more recent data-driven algorithms. In the first approach, we build upon the univariate random-walk Metropolis-Hastings MCMC, an extensively used sampling method to sample from the posterior distribution of model parameters given the data. We devise an efficient adaptive multivariate MCMC scheme, relying upon the assumption that groups of model parameters can be jointly estimated if a proper covariance matrix is defined. In doing so, our algorithm increases the sampling efficiency, while preserving accuracy and precision of estimates. We show results using both synthetic and in-vivo dMRI data. In the second approach, we resort to Simulation-Based Inference (SBI), a data-driven approach that avoids the need for iterative model inversions. This is achieved by using neural density estimators to learn the inverse mapping from the forward generative process (simulations) to the parameters of interest that have generated those simulations. By addressing the problem via learning approaches offers the opportunity to achieve inference amortisation, boosting efficiency by avoiding the necessity of repeating the inference process for each new unseen dataset. It also allows inversion of forward processes (i.e. a series of processing steps) rather than only models. We explore different neural network architectures to perform conditional density estimation of the posterior distribution of parameters. Results and comparisons obtained against MCMC suggest speed-ups of 2-3 orders of magnitude in the inference process while keeping the accuracy in the estimates

    Developing active biomaterials for implantable devices: platforms to investigate capacitive charge based control of biofouling

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    Implantable devices, in particular biosensors, have clear utility within medicine, but face a hurdle to long-term function due to adsorption of biomolecules (biofouling) and subsequent immune re- sponse to implants, the foreign body response (FBR). Strategies to control this immune reaction have included material selection, drug release and, more recently, engineered surface properties. The increasing use of embedded electronics within many classes of implanted devices presents an opportunity to exploit electromagnetic phenomena at the device surface to mitigate biofouling and FBR. Such active biomaterials would allow dynamic modification of the apparent material properties of an implanted device. A hypothesis was developed that biological interaction with a biomaterial surface can be altered by capacitive charging. A platform was constructed to test this and related hypotheses around cell and protein surface interactions in vitro and adapted into a second platform for initial characterisa- tion work on an early in vivo model using chick eggs. These platforms were designed to be easy to fabricate and to provide multiple electrical connections into a substrate in contact with biological solutions or tissue. Electrodes were fabricated from fluoropolymer coated tantalum pentoxide, a high-κ dielectric, and compared against adjacent, identically coated, silicon dioxide regions. Cells from the MDA- MB-231 cancer cell line were cultured on these regions under electrical stimulation. A voltage de- pendent reduction of cell attachment and spreading was detected on capacitively charged surfaces compared to uncharged controls. The tentative results, suggest capacitively charged surfaces hold promise as active biomaterials. A second cell type MCF-7 did not reproduce the effect, implying a more coherent understanding is required of the mechanisms behind cell surface interactions on these surfaces. Multiple independent bioelectrochemical cell-surface interactions were observed using the plat- form and several quantification techniques were successfully employed. It is therefore argued that the platform may have wide applicability as a future research tool

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    Evaluation of image quality and reconstruction parameters in recent PET-CT and PET-MR systems

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    In this PhD dissertation, we propose to evaluate the impact of using different PET isotopes for the National Electrical Manufacturers Association (NEMA) tests performance evaluation of the GE Signa integrated PET/MR. The methods were divided into three closely related categories: NEMA performance measurements, system modelling and evaluation of the image quality of the state-of-the-art of clinical PET scanners. NEMA performance measurements for characterizing spatial resolution, sensitivity, image quality, the accuracy of attenuation and scatter corrections, and noise equivalent count rate (NECR) were performed using clinically relevant and commercially available radioisotopes. Then we modelled the GE Signa integrated PET/MR system using a realistic GATE Monte Carlo simulation and validated it with the result of the NEMA measurements (sensitivity and NECR). Next, the effect of the 3T MR field on the positron range was evaluated for F-18, C-11, O-15, N-13, Ga-68 and Rb-82. Finally, to evaluate the image quality of the state-of-the-art clinical PET scanners, a noise reduction study was performed using a Bayesian Penalized-Likelihood reconstruction algorithm on a time-of-flight PET/CT scanner to investigate whether and to what extent noise can be reduced. The outcome of this thesis will allow clinicians to reduce the PET dose which is especially relevant for young patients. Besides, the Monte Carlo simulation platform for PET/MR developed for this thesis will allow physicists and engineers to better understand and design integrated PET/MR systems

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
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