39 research outputs found

    Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

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    The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p

    Photonic Tuning of Nanophosphor Transparent thin films

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    Chromaticity coordinates of Dy3+- based nanocrystals deposited as thin films are modulated from blue to yellow using photonic multilayers that are transparent in the UV and resonant in the visible part of the electromagnetic spectrumPeer reviewe

    Highly Efficient Transparent Nanophosphor Films for Tunable White-Light-Emitting Layered Coatings

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    Bright luminescence in rare-earth (RE) nanocrystals, the so-called nanophosphors, is generally achieved by choosing a host that enables an effective excitation of the RE activator through charge or energy transfer. Although tungstate, molybdate, or vanadate compounds provide the aforementioned transfer, a comparative analysis of the efficiency of such emitters remains elusive. Herein, we perform a combined structural and optical analysis, which reveals that the tetragonal GdVO matrix gives rise to the highest efficiency among the different transparent nanophosphor films compared. Then, we demonstrate that by a sequential stacking of optical quality layers made of Eu- and Dy-doped nanocrystals, it is possible to attain highly transparent white-light-emitting coatings of tunable shade with photoluminescence quantum yields above 35%. Layering provides a precise dynamic tuning of the chromaticity based on the photoexcitation wavelength dependence of the emission of the nanophosphor ensemble without altering the chemical composition of the emitters or degrading their efficiency. The total extinction of the incoming radiation along with the high quantum yields achieved makes these thin-layered phosphors one of the most efficient transparent white converter coatings ever developed.Peer Reviewe

    Efficient transparent white light emitting layered phosphor structure of tunable shade, process for obtaining said structure and uses

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    The present invention discloses a novel transparent, efficient and tuneable white light emitting phosphor structure based on thin sintered nanophosphor layers. Furthermore, the present invention refers to the process for obtaining said the transparent layered phosphor structure by means of dip coating technique and thermal annealing. Moreover, the present invention refers to the uses of said phosphor structure for the emission of white color of tunable chromaticity, as part of a device.Peer reviewedConsejo Superior de Investigaciones Científicas (España)A1 Solicitud de patente con informe sobre el estado de la técnic

    Real-time topology optimization based on convolutional neural network by using retrain skill

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    To realize a real-time structural topology optimization (TO), it is essential to use the information during the TO process. A step-to-step training method is proposed to improve the deep learning model prediction accuracy based on the solid isotropic material with penalization (SIMP) TO method. By increasing the use of optimization history information (such as the structure density matrix), the step-to-step method improves the model utilization efficiency for each sample data. This training method can effectively improve the deep learning model prediction accuracy without increasing the sample set size. The step-to-step training method combines several independent deep learning models (sub-models). The sub-models could have the same model layers and hyperparameters. It can be trained in parallel to speed up the training process. During the deep learning model training process, these features reduce the difficulties in adjusting sub-model parameters and the model training time cost. Meanwhile, this method is achieved by the local end-to-end training process. During the deep learning model predicting process, the increase in total prediction time cost can be ignored. The trained deep learning models can predict the optimized structures in real time. Maximization of first eigenfrequency topology optimization problem with three constraint conditions is used to verify the effectiveness of the proposed training method. The method proposed in this study provides an implementation technology for the real-time TO of structures. The authors also provide the deep learning model code and the dataset in this manuscript (git-hub)

    Tamm Plasmons Directionally Enhance Rare-Earth Nanophosphor Emission

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    Rare-earth-based phosphors are the materials on which current solid-state lighting technology is built. However, their large crystal size impedes the tuning, optimization, or manipulation of emitted light that can be achieved by their integration in nanophotonic architectures. Herein we demonstrate a hybrid plasmonic−photonic architecture capable of both channeling in a specific direction and enhancing by eight times the emission radiated by a macroscopically wide layer of nanophosphors. In order to do so, a slab of rare-earth-based nanocrystals is inserted between a dielectric multilayer and a metal film, following a rational design that optimizes the coupling of nanophosphor emission to collective modes sustained by the metal−dielectric system. Our approach is advantageous for the optimization of solid-state lighting systems.Peer reviewe

    Photonic structuring improves the colour purity of rare-earth nanophosphors

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    Nanophosphor integration in an optical cavity allows unprecedented control over both the chromaticity and the directionality of the emitted light, without modifying the chemical composition of the emitters or compromising their efficiency. Our approach opens a route towards the development of nanoscale photonics based solid state lighting.Peer Reviewe

    Flexible nanophosphor films doped with Mie resonators for enhanced out-coupling of the emission

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    Herein, we present a general method to prepare self-standing flexible photoluminescent coatings of controlled opacity for integration into light-emitting diodes (LEDs) employing cost-effective solution-processing methods. From colloidal suspensions of nano-sized phosphors, we fabricate light-emitting transparent films that can be doped with spherical scatterers, which act as Mie resonators that trigger a controlled photoluminescence enhancement, evidenced by the reduction of the guided light along the layer. This results in an enhanced emission compared to that extracted from a bare phosphor layer. We show not only that emission is visible under ultraviolet-LED illumination for both rigid and flexible versions of the coatings, but we also prove the feasibility of the integration of these flexible conversion layers into such devices. We believe these results can contribute to develop more efficient and cost-effective illumination sources by providing efficient and easy-to-handle conversion layers susceptible to excitation by LEDs emitting at wavelengths in the near UV region.Peer Reviewe
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