76 research outputs found

    2D open loop trajectory control of a micro-object in a dielectrophoresis-based device.

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    International audienceIn the last years, industries have shown a global trend to miniaturize the size of the components to micron in order to reduce the dimension of the final product. At this scale, a micro-object behaves differently from the micro-scale and its behavior is affected by additional physical phenomenon such as the dielectrophoresis. Dielectrophoresis (DEP) is used to separate, manipulate and detect micro particles in several domains with high speed and precision, such as in biological cell or Carbon Nano-Tubes (CNTs) manipulations. This paper focuses on developing a 2D direct dynamic model of the microobject behavior on the base of a 3D dielectrophoretic simulator. This 2D dynamic model is used to establish an open loop control law by a numerical inversion. Exploiting this control law, a high speed trajectory tracking and high precision positioning can be achieved. Several simulated and experimental results are shown to evaluate this control strategy and discuss its performance

    2D robotic control of a planar dielectrophoresis-based system.

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    International audienceNanosciences have recently proposed a lot of proofs of concept of innovative nanocomponents and especially nanosensors. Going from the current proofs of concept on this scale to reliable industrial systems requires the emergence of a new generation of manufacturing methods able to move, position and sort micro-nano-components. We propose to develop 'No Weight Robots-NWR' that use non-contact transmission of movement (e.g. dielectrophoresis, magnetophoresis) to manipulate micro-nano-objects which could enable simultaneous high throughput and high precision. This paper focuses on developing a 2D robotic control of the trajectory of a microobject manipulated by a dielectrophoresis system. A 2D dynamic model is used to establish an open loop control law by a numerical inversion. Exploiting this control law, a high speed trajectory tracking (10 Hz) and high precision positioning can be achieved. Several simulated and experimental results are shown to evaluate this control strategy and discuss its performance

    Terahertz frequency-wavelet domain deconvolution for stratigraphic and subsurface investigation of art painting

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    Terahertz frequency-wavelet deconvolution is utilized specifically for the stratigraphic and subsurface investigation of art paintings with terahertz reflective imaging. In order to resolve the optically thin paint layers, a deconvolution technique is enhanced by the combination of frequency-domain filtering and stationary wavelet shrinkage, and applied to investigate a mid-20th century Italian oil painting on paperboard, After Fishing, by Ausonio Tanda. Based on the deconvolved terahertz data, the stratigraphy of the painting including the paint layers is reconstructed and subsurface features are clearly revealed, demonstrating that terahertz frequencywavelet deconvolution can be an effective tool to characterize stratified systems with optically thin layers

    Tissue-specific suppression of thyroid hormone signaling in various mouse models of aging

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    DNA damage contributes to the process of aging, as underscored by premature aging syndromes caused by defective DNA repair. Thyroid state changes during aging, but underlying mechanisms remain elusive. Since thyroid hormone (TH) is a key regulator of metabolism, changes in TH signaling have widespread effects. Here, we reveal a significant common transcriptomic signature in livers from hypothyroid mice, DNA repair-deficient mice with severe (Csbm/m/Xpa-/-) or intermediate (Ercc1-/Δ-7) progeria and naturally aged mice. A strong induction of TH-inactivating deiodinase D3 and decrease of TH-activating D1 activities are observed in Csbm/m/Xpa-/- livers. Similar findings are noticed in Ercc1-/Δ-7, in naturally aged animals and in wild-type mice exposed to a chronic subtoxic dose of DNAdamaging agents. In contrast, TH signaling in muscle, heart and brain appears unaltered. These data show a strong suppression of TH signaling in specific peripheral organs in premature and normal aging, probably lowering metabolism, while other tissues appear to preserve metabolism. D3-mediated TH inactivation is unexpected, given its expression mainly in fetal tissues. Our studies highlight the importance of DNA damage as the underlying mechanism of changes in thyroid state. Tissue-specific regulation of deiodinase activities, ensuring diminished TH signaling, may contribute importantly to the protective metabolic response in aging

    HIF-driven SF3B1 induces KHK-C to enforce fructolysis and heart disease.

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    Fructose is a major component of dietary sugar and its overconsumption exacerbates key pathological features of metabolic syndrome. The central fructose-metabolising enzyme is ketohexokinase (KHK), which exists in two isoforms: KHK-A and KHK-C, generated through mutually exclusive alternative splicing of KHK pre-mRNAs. KHK-C displays superior affinity for fructose compared with KHK-A and is produced primarily in the liver, thus restricting fructose metabolism almost exclusively to this organ. Here we show that myocardial hypoxia actuates fructose metabolism in human and mouse models of pathological cardiac hypertrophy through hypoxia-inducible factor 1α (HIF1α) activation of SF3B1 and SF3B1-mediated splice switching of KHK-A to KHK-C. Heart-specific depletion of SF3B1 or genetic ablation of Khk, but not Khk-A alone, in mice, suppresses pathological stress-induced fructose metabolism, growth and contractile dysfunction, thus defining signalling components and molecular underpinnings of a fructose metabolism regulatory system crucial for pathological growth

    Overview on Multienzymatic Cascades for the Production of Non-canonical α-Amino Acids

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    SM-R thanks the University of Granada for the support provided by project PPJI2017-1 and the European Cooperation in Science and Technology (COST Action CA15133). Authors are also grateful to the Andalusian Regional Government through Endocrinology & Metabolism Group (CTS-202).The 22 genetically encoded amino acids (AAs) present in proteins (the 20 standard AAs together with selenocysteine and pyrrolysine), are commonly referred as proteinogenic AAs in the literature due to their appearance in ribosome-synthetized polypeptides. Beyond the borders of this key set of compounds, the rest of AAs are generally named imprecisely as non-proteinogenic AAs, even when they can also appear in polypeptide chains as a result of post-transductional machinery. Besides their importance as metabolites in life, many of D-α- and L-α-“non-canonical” amino acids (NcAAs) are of interest in the biotechnological and biomedical fields. They have found numerous applications in the discovery of new medicines and antibiotics, drug synthesis, cosmetic, and nutritional compounds, or in the improvement of protein and peptide pharmaceuticals. In addition to the numerous studies dealing with the asymmetric synthesis of NcAAs, many different enzymatic pathways have been reported in the literature allowing for the biosynthesis of NcAAs. Due to the huge heterogeneity of this group of molecules, this review is devoted to provide an overview on different established multienzymatic cascades for the production of non-canonical D-α- and L-α-AAs, supplying neophyte and experienced professionals in this field with different illustrative examples in the literature. Whereas the discovery of new or newly designed enzymes is of great interest, dusting off previous enzymatic methodologies by a “back and to the future” strategy might accelerate the implementation of new or improved multienzymatic cascades.University of Granada PPJI2017-1European Cooperation in Science and Technology (COST) CA15133Andalusian Regional Government through Endocrinology & Metabolism Group CTS-20

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches
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