9,785 research outputs found
Innovative and eco-friendly approaches to mitigate Microbiologically Influenced Corrosion
No full textMicrobiologically influenced corrosion (MIC) is a type of corrosion caused or accelerated by microorganisms, such as bacteria, fungi, and algae. The metabolic activity of corrosive microorganisms influences the electrochemical processes that degrade materials, especially metals, leading to pitting or crevice formation that significantly increase their deterioration rate. MIC affects various industries, including oil and gas, petrochemical, water treatment, marine, as well as any system where metals are exposed to water, soil, or humid environments. The economic burden of MIC is immense, contributing up to 20% of global corrosion-related costs, approximately US$ 2.5 trillion annually in the oil and gas sector alone, excluding its safety and environmental impacts. Counteracting MIC requires a combination of preventative and corrective measures. Sulfate-reducing bacteria (SRB) are the major contributors to MIC due to their production of hydrogen sulfide, which accelerates corrosion. Effective strategies for eliminating or controlling SRB involve both physical and chemical methods. These last face challenges such as toxicity and high disposal costs, calling for sustainable alternatives.
This PhD thesis explore two innovative and eco-friendly approaches with the aim of contributing to the development of novel strategies to manage and control MIC. The first approach investigates the potential use of cinnamaldehyde, a compound with well-documented antimicrobial and anticorrosive properties. We found that low concentration of cinnamaldehyde (i.e., 12.5 μg/ml) inhibited the growth and killed Desulfovibrio vulgaris planktonic cells and almost eradicate pre-formed biofilms at 50 μg/ml, by reducing biomass (> 90 %), surface area (> 85 %) and thickness (> 60 %), with comparable efficacy to the conventional biocide, glutaraldehyde. Interestingly, we were able to show that cinnamaldehyde effectively disrupts pre-formed D. vulgaris biofilms also on representative metal coupons. These results pave the way for the future development of green sustainable strategies involving the use of cinnamaldehyde to mitigate MIC. The second approach regards the application of endolysins to control the SRB growth. Endolysins are hydrolytic enzymes encoded by bacteriophages during their lytic cycle, targeting the bacterial peptidoglycan layer, thus promoting osmotic lysis. Their fast lytic activity can also be accomplished when exogenously applied as recombinant proteins. These enzymes have garnered significant attention for their efficacy against clinically-relevant pathogens and are currently employed in clinical settings. However, their application in environmental contexts, particularly those impacted by MIC remains largely unexplored. We have selected and tested D. vulgaris-specific endolysins, demonstrating their effectiveness against D. vulgaris planktonic cells. Although preliminary, these promising results highlight the potential of endolysins in the sustainable management of MIC, bypassing the use of conventional toxic biocides. Finally, a case study highlighting the importance of microbiological investigations as a proactive MIC prevention strategy, to advocate for the incorporation of microbial community characterization into MIC management practices. Such proactive approaches will offer a potential to improve early detection and mitigation strategies, protecting marine infrastructures from corrosion-related failures
ANTIGEN TARGETING TO ANTIGEN-PRESENTING CELLS ENHANCES PRESENTATION TO CLASS II-RESTRICTED T-LYMPHOCYTES
No full textReceptor-mediated uptake increases by several orders of magnitude the efficiency of APC to internalize Ag, and is stringently required for the Ag-presenting function of T lymphocytes due to their inability to take up Ag non-specifically. We have previously reported that hepatitis B envelope antigen (HBenvAg) can be internalized by T cells via transferrin receptor (TfR). To evaluate if Ag targeting to receptors expressed on APC could be an effective tool for promoting Ag uptake and presentation, we tested the capacity of activated T cells not expressing TfR to induce HBenvAg-specific T-cell responses when pulsed with a hybrid particle containing HBenvAg coupled to gp120 of human immunodeficiency virus (HIV), exploiting the ability of gp120 to bind to CD4 receptor. We found that CD4(+)/TfR(-) T cells pulsed either with the hybrid particle or peptide (S-193-207) but not with S,L Ag, a recombinant form of HBenvAg, induced a specific proliferative response of a T-cell clone recognizing peptide (S-193-207) Of HBenvAg. The finding that the addition of anti-CD4 monoclonal antibody (mAb) before the pulsing of CD4(+)/TfR(-) T cells with the hybrid particle drastically blocked the specific T-cell response, together with the finding that CD8(+)/TfR(-) T cells were unable to serve as APC even if pulsed with this molecule, demonstrated that CD4 receptor was crucial for the HBenvAg internalization. On the other hand, HBenvAg presentation by CD4(+)/TfR(+) T cells pulsed with the hybrid particle was inhibited only when both anti-CD4 and anti-TfR were added before the pulsing. These results suggest that Ag targeting to APC receptors may be usefully exploited to improve Ag-presentation efficiency in potential immunotherapeutic approaches
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
Endocrine involvement in children with beta-thalassemia major. Transversal and longitudinal studies. I: Pituitary- thyroidal axis function and its correlation with serum ferritin levels
No full textMachine Learning Toxicity Prediction: Latest Advances by Toxicity End Point
No full textMachine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted.Fil: Cavasotto, Claudio Norberto. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; ArgentinaFil: Scardino, Valeria. Universidad Austral; Argentin
Molecularly imprinted polymer for solid phase extraction of nicotinamide in pork liver samples
No full textNicotinamide (NAM) is often added in fortified infant and various food products to ensure an adequate consumption of vitamin. Thus, a proper monitoring of NAM content in foods can be important. In this study, a selective molecularly imprinted polymer as sorbent for solid phase extraction of NAM in animal sources was successfully developed. The molecularly imprinted polymer was synthesized by bulk polymerization technique. The performances of this polymer as sorbent were investigated in NAM standard solutions. One hundred milligrams of polymer was able to retain up to 244 μg of NAM with recovery >80% when chloroform was used as loading and washing solvent and ethanol as eluting solvent. Other solvent mixtures were also tested. The optimal molecularly imprinted solid phase extraction protocol was defined and used for the clean-up of NAM in pork liver samples. Moreover, the performances of the imprinted polymer were compared with that of nonimprinted polymer and with conventional reversed-phase C18 performances. Pork liver samples spiked with 49 μg mL−1 gave a good percentage of recovery of 87%, with relative standard deviation of 8% for imprinted polymer, whereas only 12% of recovery for nonimprinted polymer and 14% of recovery for reversed-phase C18 sorbent were foun
Avaliação do rastreamento para câncer de prostata em homens com antígeno prostático específico menor que 3,0 ng/ml.
No full textTrabalho de Conclusão de Curso - Universidade Federal de Santa Catarina. Curso de Medicina. Dapartamento de Clínica Cirúrgica
Building a generalisable ML pipeline at ING
No full textAdvances in data science have caused an increase in the use of Artificial Intelligence (AI), specifically Machine Learning (ML), throughout various fields. Not only in research but in the industry as well, has ML been receiving increasing amounts of interest. Many companies rely on ML models to increase the efficiency of existing processes or offer new services and products. The industry, however, is facing several additional challenges compared to the academic context. One of those challenges is applying the Development Operations (DevOps) model to an ML application, also referred to as MLOps. This thesis sets out to find the specific challenges that practitioners encounter while operationalising ML models. To do so, we perform a single-case case study on an ML pipeline built by the Trade & Communication Surveillance team at the ING bank. This case study consists of conducting a set of interviews and performing a manual code inspection of the pipeline. The team faces challenges ranging from having insufficient time for operationalising each ML project individually to operating in the highlyregulated fintech context. Their pipeline is able to deploy a single ML model but it does not generalise well to other projects. We present the first version of an application that mitigates these challenges. The application is able to deploy ML models to the development environment at ING and can be operated by data scientists to reduce the effort of operationalising an ML model. Computer Science | Software Technolog
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