Magnetic modulation of far- and near-field IR properties in rod-slit complementary spintronic metasurfaces.

Complementary metasurfaces composed of randomly-placed arrays of aligned rods or slits are fabricated out of giant magnetoresistance Ni81Fe19/Au multilayers (MLs), a material whose optical properties change under the application of an external static magnetic field. The two metasurfaces are studied from both the experimental and theoretical viewpoints. The induced magnetic modulation (MM) of both the far-field signal and the resonant near field, at the rod/slit localized surface plasmon frequency, are found to obey the Babinet's principle. Furthermore, the near-field MM is found to be higher than the far-field counterpart. At resonance, both arrays show spots with high values of the magnetic modulated intensity of the electric near field (MM hot-spots). We show that this high magnetic modulation of the near-field intensity is very promising for the future development of high sensitivity molecular sensing platforms in the Mid- and Far-IR, using Magnetic-Modulation of Surface-Enhanced Infrared Absorption (MM-SEIRA) spectroscopy.

Scoring pleurisy in slaughtered pigs using convolutional neural networks.

Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry.

Anisotropic Nanoantenna-Based Magnetoplasmonic Crystals for Highly Enhanced and Tunable Magneto-Optical Activity.

We present a novel concept of a magnetically tunable plasmonic crystal based on the excitation of Fano lattice surface modes in periodic arrays of magnetic and optically anisotropic nanoantennas. We show how coherent diffractive far-field coupling between elliptical nickel nanoantennas is governed by the two in-plane, orthogonal and spectrally detuned plasmonic responses of the individual building block, one directly induced by the incident radiation and the other induced by the application of an external magnetic field. The consequent excitation of magnetic field-induced Fano lattice surface modes leads to highly tunable and amplified magneto-optical effects as compared to a continuous film or metasurfaces made of disordered noninteracting magnetoplasmonic anisotropic nanoantennas. The concepts presented here can be exploited to design novel magnetoplasmonic sensors based on coupled localized plasmonic resonances, and nanoscale metamaterials for precise control and magnetically driven tunability of light polarization states.

Amplifying Sensing Capabilities: Combining Plasmonic Resonances and Fresnel Reflections through Multivariate Analysis.

Multivariate analysis applied in biosensing greatly improves analytical performance by extracting relevant information or bypassing confounding factors such as nonlinear responses or experimental errors and noise. Plasmonic sensors based on various light coupling mechanisms have shown impressive performance in biosensing by detecting dielectric changes with high sensitivity. In this study, gold nanodiscs are used as metasurface in a Kretschmann setup, and a variety of features from the reflectance curve are used by machine learning to improve sensing performance. The nanostructures of the metasurface generate new plasmonic features, apart from the typical resonance that occurs in the classical Kretschmann mode of a gold thin film, related to the evanescent field beyond total internal reflection. When the engineered metasurface is integrated into a microfluidic chamber, the device provides additional spectral features generated by Fresnel reflections at all dielectric interfaces. The increased number of features results in greatly improved detection. Here, multivariate analysis enhances analytical sensitivity and sensor resolution by 200% and more than 20%, respectively, and reduces prediction errors by almost 40% compared to a standard plasmonic sensor. The combination of plasmonic metasurfaces and Fresnel reflections thus offers the possibility of improving sensing capabilities even in commonly available setups.

Warp and Learn: Novel Views Generation for Vehicles and Other Objects.

In this article we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from parametric (i.e., entirely learning-based) methods, we show how a-priori geometric knowledge about the object and the 3D world can be successfully integrated into a deep learning based image generation framework. As this geometric component is not learnt, we call our approach semi-parametric. In particular, we exploit man-made object symmetry and piece-wise planarity to integrate rich a-priori visual information into the novel viewpoint synthesis process. An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance. This careful blend between parametric and non-parametric components allows us to i) operate in a real-world scenario, ii) preserve high-frequency visual information such as textures, iii) handle truly arbitrary 3D roto-translations of the input, and iv) perform shape transfer to completely different 3D models. Eventually, we show that our approach can be easily complemented with synthetic data and extended to other rigid objects with completely different topology, even in presence of concave structures and holes (e.g., chairs). A comprehensive experimental analysis against state-of-the-art competitors shows the efficacy of our method both from a quantitative and a perceptive point of view. Supplementary material, animated results, code, and data are available at: https://github.com/ndrplz/semiparametric.

Gait-Based Diplegia Classification Using LSMT Networks.

Diplegia is a specific subcategory of the wide spectrum of motion disorders gathered under the name of cerebral palsy. Recent works proposed to use gait analysis for diplegia classification paving the way for automated analysis. A clinically established gait-based classification system divides diplegic patients into 4 main forms, each one associated with a peculiar walking pattern. In this work, we apply two different deep learning techniques, namely, multilayer perceptron and recurrent neural networks, to automatically classify children into the 4 clinical forms. For the analysis, we used a dataset comprising gait data of 174 patients collected by means of an optoelectronic system. The measurements describing walking patterns have been processed to extract 27 angular parameters and then used to train both kinds of neural networks. Classification results are comparable with those provided by experts in 3 out of 4 forms.

Antenna-assisted picosecond control of nanoscale phase transition in vanadium dioxide.

Nanoscale devices in which the interaction with light can be configured using external control signals hold great interest for next-generation optoelectronic circuits. Materials exhibiting a structural or electronic phase transition offer a large modulation contrast with multi-level optical switching and memory functionalities. In addition, plasmonic nanoantennas can provide an efficient enhancement mechanism for both the optically induced excitation and the readout of materials strategically positioned in their local environment. Here, we demonstrate picosecond all-optical switching of the local phase transition in plasmonic antenna-vanadium dioxide (VO2) hybrids, exploiting strong resonant field enhancement and selective optical pumping in plasmonic hotspots. Polarization- and wavelength-dependent pump-probe spectroscopy of multifrequency crossed antenna arrays shows that nanoscale optical switching in plasmonic hotspots does not affect neighboring antennas placed within 100 nm of the excited antennas. The antenna-assisted pumping mechanism is confirmed by numerical model calculations of the resonant, antenna-mediated local heating on a picosecond time scale. The hybrid, nanoscale excitation mechanism results in 20 times reduced switching energies and 5 times faster recovery times than a VO2 film without antennas, enabling fully reversible switching at over two million cycles per second and at local switching energies in the picojoule range. The hybrid solution of antennas and VO2 provides a conceptual framework to merge the field localization and phase-transition response, enabling precise, nanoscale optical memory functionalities.

Non-linear optical response by functionalized gold nanospheres: identifying design principles to maximize the molecular photo-release.

In a recent study by Voliani et al. [Small, 2011, 7, 3271], the electromagnetic field enhancement in the vicinity of the gold nanoparticle surface has been exploited to achieve photocontrolled release of a molecular cargo conjugated to the nanoparticles via 1,2,3-triazole, a photocleavable moiety. The aim of the present study is to investigate the mechanism of the photorelease by characterizing the nanoparticle aggregation status within the cells and simulating the electric field enhancement in a range of experimentally realistic geometries, such as single Au nanoparticles, dimers, trimers and random aggregates. Two plasmon-enhanced processes are examined for triazole photocleavage, i.e. three-photon excitation and third-harmonic-generation (one-photon) excitation. Taking into account the absorption cross sections of the triazole, we conclude that the latter mechanism is more efficient, and provides a photocleavage rate that explains the experimental findings. Moreover, we determine which aggregate geometries are required to maximize the field enhancement, and the dependence of such enhancement on the excitation wavelength. Our results provide design principles for maximizing the multiphoton molecular photorelease by such functionalized gold nanoparticles.

Postpartum thrombotic complication in a patient with paroxysmal nocturnal hemoglobinuria.

Paroxysmal nocturnal hemoglobinuria (PNH) is a clonal nonneoplastic hematopoietic stem cell disease characterized by an acquired mutation of the PIG-A gene with reduction or absence of CD55 and CD59. The absence of these proteins renders PNH erythrocytes susceptible to complement-mediated hemolysis. We report the case of a PNH patient before and during pregnancy until delivery. We observed and treated some postpartum thrombotic complications. Eculizumab should be used with caution in pregnancy. There are several reports supporting its use in these patients. This case should be considered paradigmatic of a series of clinical situations that may occur in the course of a pregnancy in patients with PNH: increased need for transfusion, need to increase the dose of Eculizumab, and insurgence of fetal sufferance. Moreover, after delivery, the patient, despite adequate prophylaxis with low-molecular-weight heparins, presented severe complications: development of pleural and peritoneal effusion, pulmonary embolism, bilateral upper limbs thrombophlebitis, and a possible abdominal angina with a transient paralytic ileus. All these complications were overcome and now the baby is healthy and the mother has returned to the usual therapeutic regimen.

Influence of size, shape and core-shell interface on surface plasmon resonance in Ag and Ag@MgO nanoparticle films deposited on Si/SiO x.

Ag and Ag@MgO core-shell nanoparticles (NPs) with a diameter of d = 3-10 nm were obtained by physical synthesis methods and deposited on Si with its native ultrathin oxide layer SiO x (Si/SiO x ). Scanning electron microscopy and transmission electron microscopy (TEM) images of bare Ag NPs revealed the presence of small NP aggregates caused by diffusion on the surface and agglomeration. Atomic resolution TEM gave evidence of the presence of crystalline multidomains in the NPs, which were due to aggregation and multitwinning occurring during NP growth in the nanocluster source. Co-deposition of Ag NPs and Mg atoms in an oxygen atmosphere gave rise to formation of a MgO shell matrix surrounding the Ag NPs. The behaviour of the surface plasmon resonance (SPR) excitation in surface differential reflectivity (SDR) spectra with p-polarised light was investigated for bare Ag and Ag@MgO NPs. It was shown that the presence of MgO around the Ag NPs caused a red shift of the plasmon excitation, and served to preserve its existence after prolonged (five months) exposure to air, realizing the possibility of technological applications in plasmonic devices. The Ag NP and Ag@MgO NP film features in the SDR spectra could be reproduced by classical electrodynamics simulations by treating the NP-containing layer as an effective Maxwell Garnett medium. The simulations gave results in agreement with the experiments when accounting for the experimentally observed aggregation.