Experimental observation of Dyakonov plasmons in the mid-infrared

AbstractIn this work, we report on observation of Dyakonov plasmons at an interface with a hyperbolic metamaterial in the mid-IR. The hyperbolic metamaterial is implemented as a CMOS-compatible high aspect ratio grating structure with aluminium-doped ZnO (AZO) ridges grown by atomic layer deposition in deep trench silicon matrix. The dispersion of Dyakonov plasmons is characterized by the attenuated total reflection method in the Otto configuration. We demonstrate that Dyakonov plasmons propagate in a broad range of directions (a few tens of degrees) in contrast to the classical Dyakonov surface waves (about one tenth of degree). The obtained results provide useful guidelines for practical implementations of structures supporting Dyakonov plasmons in the mid-IR. </jats:p

Cutoff-Free Traveling Wave NMR

Recently, the concept of traveling-wave NMR/MRI was introduced by Brunner et al. (Nature 457, 994-992 (2009)), who demonstrated MR images acquired using radio frequency (RF) waves propagating down the bore of an MR scanner. One of the significant limitations of this approach is that each bore has a specific cutoff frequency, which can be higher than most Larmor frequencies of at the magnetic field strengths commonly in use for MR imaging and spectroscopy today. We overcome this limitation by using a central conductor in the waveguide and thereby converting it to a transmission line (TL), which has no cutoff frequency. Broadband propagation of waves through the sample thus becomes possible. NMR spectra and images with such an arrangement are presented and genuine traveling wave behavior is demonstrated. In addition to facilitating NMR spectroscopy and imaging in smaller bores via traveling waves, this approach also allows one to perform multinuclear traveling wave experiments (an example of which is shown), and to study otherwise difficult-to-access samples in unusual geometries.Comment: 25 pages, 7 figures, previously presented at (1) World-Wide NMR Conference (ISMAR/Ampere joint meeting), Florence, Italy, July 9, 2010, and (2) Experimental NMR Conference, Asilomar, CA, April 13, 201

The Song Describer dataset: a corpus of audio captions for music-and-language evaluation

We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models. The dataset consists of 1.1k human-written natural language descriptions of 706 music recordings, all publicly accessible and released under Creative Common licenses. To showcase the use of our dataset, we benchmark popular models on three key music-and-language tasks (music captioning, text-to-music generation and music-language retrieval). Our experiments highlight the importance of cross dataset evaluation and offer insights into how researchers can use SDD to gain a broader understanding of model performance

Midinfrared Surface Waves on a High Aspect Ratio Nanotrench Platform

Optical surface waves, highly localized modes bound to the surface of media, enable manipulation of light at nanoscale, thus impacting a wide range of areas in nanoscience. By applying metamaterials, artificially designed optical materials, as contacting media at the interface, we can significantly ameliorate surface wave propagation and even generate new types of waves. Here, we demonstrate that high aspect ratio (1:20) grating structures with plasmonic lamellas in deep nanoscale trenches, whose pitch is 1/10–1/35 of a wavelength, function as a versatile platform supporting both surface and guided bulk infrared waves. The surface waves exhibit a unique combination of properties: directionality, broadband existence (from 4 μm to at least 14 μm and beyond) and high localization, making them an attractive tool for effective control of light in an extended range of infrared frequencies

Establishing Correlations between Breast Tumor Response to Radio-Immunotherapy and Radiomics from Multi-Parametric Imaging: An Animal Study

Triple-negative breast cancer (TNBC), which is a type of invasive breast cancer, is characterized by severe disease progression, poor prognosis, high recurrence rate, and short survival. We sought to gain new insight into TNBC by applying computed tomography (CT) and magnetic resonance (MR) quantitative imaging (radiomics) approaches to predict the outcome of radio-immunotherapy treatments in a syngeneic subcutaneous murine breast tumor model. Five Athymic Nude mice were implanted with breast cancer cell lines (4T1) tumors on the right flank. The animals were CT- and MRI-imaged, tumors were contoured, and radiomics features were extracted. All animals were treated with radiotherapy (RT), followed by the administration of PD1 inhibitor. Approximately 10 days later, the animals were sacrificed, tumor volumes were measured, and histopathology evaluation was performed through Ki-67 staining. Linear regression modeling between radiomics and Ki-67 results was performed to establish a correlation between quantitative imaging and post-treatment histochemistry. There was no correlation between tumor volumes and Ki-67 values. Multiple CT- and MRI-derived features, however, correlated with histopathology with correlation coefficients greater than 0.8. MRI imaging helps in tumor delineation as well as an additional orthogonal imaging modality for quantitative imaging purposes. This is the first investigation correlating simultaneously CT- and MRI-derived radiomics to histopathology outcomes of combined radio-immunotherapy treatments in a preclinical setting applied to treatment na&iuml;ve tumors. The findings indicate that imaging can guide discrimination between responding and non-responding tumors for the combined RT and ImT treatment regimen in TNBC

Establishing Correlations between Breast Tumor Response to Radio-Immunotherapy and Radiomics from Multi-Parametric Imaging: An Animal Study

Triple-negative breast cancer (TNBC), which is a type of invasive breast cancer, is characterized by severe disease progression, poor prognosis, high recurrence rate, and short survival. We sought to gain new insight into TNBC by applying computed tomography (CT) and magnetic resonance (MR) quantitative imaging (radiomics) approaches to predict the outcome of radio-immunotherapy treatments in a syngeneic subcutaneous murine breast tumor model. Five Athymic Nude mice were implanted with breast cancer cell lines (4T1) tumors on the right flank. The animals were CT- and MRI-imaged, tumors were contoured, and radiomics features were extracted. All animals were treated with radiotherapy (RT), followed by the administration of PD1 inhibitor. Approximately 10 days later, the animals were sacrificed, tumor volumes were measured, and histopathology evaluation was performed through Ki-67 staining. Linear regression modeling between radiomics and Ki-67 results was performed to establish a correlation between quantitative imaging and post-treatment histochemistry. There was no correlation between tumor volumes and Ki-67 values. Multiple CT- and MRI-derived features, however, correlated with histopathology with correlation coefficients greater than 0.8. MRI imaging helps in tumor delineation as well as an additional orthogonal imaging modality for quantitative imaging purposes. This is the first investigation correlating simultaneously CT- and MRI-derived radiomics to histopathology outcomes of combined radio-immunotherapy treatments in a preclinical setting applied to treatment naïve tumors. The findings indicate that imaging can guide discrimination between responding and non-responding tumors for the combined RT and ImT treatment regimen in TNBC