1,887 research outputs found

    Aerostatic Journal Bearing based on an Orthotropic Layered Porous Structure

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    Fluidmechanische Untersuchung der Tragfähigkeit und Bewertung der pneumatischen Stabilität mikroporöser Gleitlager auf Basis aktiv transpirationsgeschmierter faserkeramischer Gleitlagerhülsen. Mit Hilfe des Darcy-Ansatzes für die Durchströmung poröser Medien wurde die Lager-Tragfähigkeit in radialer und axialer Richtung untersucht und ein Mehrwert gegenüber dem Stand der Technik dargestellt. Der Technologieansatz stammt ursprünglich aus der DLR-Entwicklung transpirationsgekühlter faserkeramischer Raketenbrennkammern und wurde gemeinsam mit der TU-Kaiserslautern auf die Lagertechnologie übertragen

    Stem Cells in Domestic Animals

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    Stem cells are an attractive tool for cell-based therapies in regenerative medicine, both for humans and animals. The research and review articles published in this first book of the Collection “Stem Cells in Domestic Animals: Applications in Health and Production” are excellent examples of the recent advances made in the field of stem/stromal cell research in veterinary medicine. In this field, sophisticated and new treatments are now required for improving patients’ quality of life; in livestock animals, the goal of regenerative medicine is to improve not only animal welfare but also the quality of production, aiming to preserve human health. The contributions collected in this book concern both laboratory research and clinical applications of mesenchymal stem/stromal cells. The increasing knowledge of cell-based therapies may constitute an opportunity for researchers, veterinary practitioners, and animal owners to contribute to animal and human health and well-being

    Machine Learning Approaches for Semantic Segmentation on Partly-Annotated Medical Images

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    Semantic segmentation of medical images plays a crucial role in assisting medical practitioners in providing accurate and swift diagnoses; nevertheless, deep neural networks require extensive labelled data to learn and generalise appropriately. This is a major issue in medical imagery because most of the datasets are not fully annotated. Training models with partly-annotated datasets generate plenty of predictions that belong to correct unannotated areas that are categorised as false positives; as a result, standard segmentation metrics and objective functions do not work correctly, affecting the overall performance of the models. In this thesis, the semantic segmentation of partly-annotated medical datasets is extensively and thoroughly studied. The general objective is to improve the segmentation results of medical images via innovative supervised and semi-supervised approaches. The main contributions of this work are the following. Firstly, a new metric, specifically designed for this kind of dataset, can provide a reliable score to partly-annotated datasets with positive expert feedback in their generated predictions by exploiting all the confusion matrix values except the false positives. Secondly, an innovative approach to generating better pseudo-labels when applying co-training with the disagreement selection strategy. This method expands the pixels in disagreement utilising the combined predictions as a guide. Thirdly, original attention mechanisms based on disagreement are designed for two cases: intra-model and inter-model. These attention modules leverage the disagreement between layers (from the same or different model instances) to enhance the overall learning process and generalisation of the models. Lastly, innovative deep supervision methods improve the segmentation results by training neural networks one subnetwork at a time following the order of the supervision branches. The methods are thoroughly evaluated on several histopathological datasets showing significant improvements

    Generalised latent variable models for location, scale, and shape parameters

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    Latent Variable Models (LVM) are widely used in social, behavioural, and educational sciences to uncover underlying associations in multivariate data using a smaller number of latent variables. However, the classical LVM framework has certain assumptions that can be restrictive in empirical applications. In particular, the distribution of the observed variables being from the exponential family and the latent variables influencing only the conditional mean of the observed variables. This thesis addresses these limitations and contributes to the current literature in two ways. First, we propose a novel class of models called Generalised Latent Variable Models for Location, Scale, and Shape parameters (GLVM-LSS). These models use linear functions of latent factors to model location, scale, and shape parameters of the items’ conditional distributions. By doing so, we model higher order moments such as variance, skewness, and kurtosis in terms of the latent variables, providing a more flexible framework compared to classical factor models. The model parameters are estimated using maximum likelihood estimation. Second, we address the challenge of interpreting the GLVM-LSS, which can be complex due to its increased number of parameters. We propose a penalised maximum likelihood estimation approach with automatic selection of tuning parameters. This extends previous work on penalised estimation in the LVM literature to cases without closed-form solutions. Our findings suggest that modelling the entire distribution of items, not just the conditional mean, leads to improved model fit and deeper insights into how the items reflect the latent constructs they are intended to measure. To assess the performance of the proposed methods, we conduct extensive simulation studies and apply it to real-world data from educational testing and public opinion research. The results highlight the efficacy of the GLVM-LSS framework in capturing complex relationships between observed variables and latent factors, providing valuable insights for researchers in various fields

    Study of the Soil Water Movement in Irrigated Agriculture

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    In irrigated agriculture, the study of the various ways water infiltrates into the soils is necessary. In this respect, soil hydraulic properties, such as soil moisture retention curve, diffusivity, and hydraulic conductivity functions, play a crucial role, as they control the infiltration process and the soil water and solute movement. This Special Issue presents the recent developments in the various aspects of soil water movement in irrigated agriculture through a number of research topics that tackle one or more of the following challenges: irrigation systems and one-, two-, and three-dimensional soil water movement; one-, two-, and three-dimensional infiltration analysis from a disc infiltrometer; dielectric devices for monitoring soil water content and methods for assessment of soil water pressure head; soil hydraulic properties and their temporal and spatial variability under the irrigation situations; saturated–unsaturated flow model in irrigated soils; soil water redistribution and the role of hysteresis; soil water movement and drainage in irrigated agriculture; salt accumulation, soil salinization, and soil salinity assessment; effect of salts on hydraulic conductivity; and soil conditioners and mulches that change the upper soil hydraulic properties and their effect on soil water movement

    Natural Products as Kinase Inhibitors: Total Synthesis, in Vitro Kinase Activity, in Vivo Toxicology in Zebrafish Embryos and in Silico Docking

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    Despite significant progress in developing small molecule kinase inhibitors, most human kinases still lack high-quality selective inhibitors that might be employed as chemical probes to study their biological function and pharmacology. Natural products (NPs) and their synthetic derivates might give avenues to overcome this frequently encountered challenge as they demonstrated to target a wide range of kinases, including all subfamilies of the known kinome. Nonetheless, isolating these NPs from their sources necessitates massive harvesting, which is fraught with difficulties and triggers enormous harm to the ecology. Moreover, the challenges encountered while extracting these NPs from their sources are constantly present and have few viable solutions. Considering these aspects, total synthesis and semisynthesis have been employed to replicate the most intriguing compounds of living nature in laboratories to obtain larger quantities for extended studies. The present work outlined the attempts to perform the first total syntheses and to evaluate the biological activity of naturally occurring potent anti-cancer compounds: Depsipeptide PM181110, Eudistomidin C, and Fusarithioamide A. Efforts to achieve the first total syntheses of these natural compounds have been based on highly convergent and unified approaches. Depsipeptide PM181110 is a bicyclic depsipeptide featuring four stereogenic centres whose attempts to perform its first total synthesis were undertaken by synthesizing its diastereomers 3R,9R,14R,17R, and 3R,9S,14R,17R. Similarly, for Eudistomidin C and Fusarithioamide A having known stereochemistry, the attempts to perform their syntheses were made starting from enantiomerically pure reagents. The synthesized compounds BSc5484, BSc5517 and the analogues were subjected to biological activity tests afterwards. Accordingly, a kinase inhibitory activity test was performed, followed by an in vivo toxicology assay in wild-type and gold-type zebrafish embryos Danio rerio. As a result, the assayed compounds displayed moderate to good inhibition of the kinases with an apparent selectivity profile and toxicity in zebrafish embryos illustrated by the observed phenotypes. Finally, an in silico experiment revealed that BSc5484 and BSc5485 might bind as type IV inhibitors, while BSc5517 demonstrated a better binding affinity to human Haspin kinase compared to the known b-carboline inhibitor Harmine across the panel of the tested kinases. This work thus provides the first directed tools about the potential of naturally derived compounds as inhibitors of disease-causing proteins that are key players in numerous forms of cancer and other illnesses. Consequently, establishing depsipeptide and b-carboline-based compounds as therapeutic leads is crucial and will provide a powerful tool to further elucidate their biological function through targeted structural variations

    Statistical methods for gene selection and genetic association studies

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    This dissertation includes five Chapters. A brief description of each chapter is organized as follows. In Chapter One, we propose a signed bipartite genotype and phenotype network (GPN) by linking phenotypes and genotypes based on the statistical associations. It provides a new insight to investigate the genetic architecture among multiple correlated phenotypes and explore where phenotypes might be related at a higher level of cellular and organismal organization. We show that multiple phenotypes association studies by considering the proposed network are improved by incorporating the genetic information into the phenotype clustering. In Chapter Two, we first illustrate the proposed GPN to GWAS summary statistics. Then, we assess contributions to constructing a well-defined GPN with a clear representation of genetic associations by comparing the network properties with a random network, including connectivity, centrality, and community structure. The network topology annotations based on the sparse representations of GPN can be used to understand the disease heritability for the highly correlated phenotypes. In applications of phenome-wide association studies, the proposed GPN can identify more significant pairs of genetic variant and phenotype categories. In Chapter Three, a powerful and computationally efficient gene-based association test is proposed, aggregating information from different gene-based association tests and also incorporating expression quantitative trait locus information. We show that the proposed method controls the type I error rates very well and has higher power in the simulation studies and can identify more significant genes in the real data analyses. In Chapter Four, we develop six statistical selection methods based on the penalized regression for inferring target genes of a transcription factor (TF). In this study, the proposed selection methods combine statistics, machine learning , and convex optimization approach, which have great efficacy in identifying the true target genes. The methods will fill the gap of lacking the appropriate methods for predicting target genes of a TF, and are instrumental for validating experimental results yielding from ChIP-seq and DAP-seq, and conversely, selection and annotation of TFs based on their target genes. In Chapter Five, we propose a gene selection approach by capturing gene-level signals in network-based regression into case-control association studies with DNA sequence data or DNA methylation data, inspired by the popular gene-based association tests using a weighted combination of genetic variants to capture the combined effect of individual genetic variants within a gene. We show that the proposed gene selection approach have higher true positive rates than using traditional dimension reduction techniques in the simulation studies and select potentially rheumatoid arthritis related genes that are missed by existing methods

    Strategies to Improve Antineoplastic Activity of Drugs in Cancer Progression

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    The aim of this Special Issue is to collect reports regarding all the recent strategies, directed at the improvement of antineoplastic activity of drugs in cancer progression, engaging all the expertise needed for the development of new anticancer drugs: medicinal chemistry, pharmacology, molecular biology, and computational and drug delivery studies

    Analysis, Design and Fabrication of Micromixers, Volume II

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    Micromixers are an important component in micrototal analysis systems and lab-on-a-chip platforms which are widely used for sample preparation and analysis, drug delivery, and biological and chemical synthesis. The Special Issue "Analysis, Design and Fabrication of Micromixers II" published in Micromachines covers new mechanisms, numerical and/or experimental mixing analysis, design, and fabrication of various micromixers. This reprint includes an editorial, two review papers, and eleven research papers reporting on five active and six passive micromixers. Three of the active micromixers have electrokinetic driving force, but the other two are activated by mechanical mechanism and acoustic streaming. Three studies employs non-Newtonian working fluids, one of which deals with nano-non-Newtonian fluids. Most of the cases investigated micromixer design
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