Dielectric spectroscopy study of thermally-aged extruded model power cables

“Model” extruded power cables, having a much reduced geometry but using the same extrusion techniques and materials as full-sized cables, have been examined using dielectric spectroscopy techniques to study their thermal ageing effects. Cables insulated with homo-polymer XLPE and co-polymer of XLPE with micron-sized ethylene-butyl-acrylate (EBA) islands were studied by both frequency-domain and time-domain dielectric spectroscopy techniques after accelerated thermal ageing under 135°C for 60 days. In the frequency domain, a frequency response analyzer (FRA) was used to measure the frequency range from 10-4Hz to 1Hz at temperatures from 20°C to 80°C. In the time domain, a special charging/discharging current measurement system was developed to measure the frequencies from 10-1Hz to 102Hz. These techniques were chosen to cope with the extremely low dielectric losses of the model cables. The results are compared with those from new model power cables that were degassed at 80°C for 5 days. Thermal ageing was found to increase the low-frequency conductivity, permittivity and the discharging current. Both homo- and co-polymer cables have substantial increase of dielectric loss after ageing

Cerebrospinal fluid metabolomics: detection of neuroinflammation in human central nervous system disease.

The high morbidity and mortality of neuroinflammatory diseases drives significant interest in understanding the underlying mechanisms involved in the innate and adaptive immune response of the central nervous system (CNS). Diagnostic biomarkers are important to define treatable neuroinflammation. Metabolomics is a rapidly evolving research area offering novel insights into metabolic pathways, and elucidation of reliable metabolites as biomarkers for diseases. This review focuses on the emerging literature regarding the detection of neuroinflammation using cerebrospinal fluid (CSF) metabolomics in human cohort studies. Studies of classic neuroinflammatory disorders such as encephalitis, CNS infection and multiple sclerosis confirm the utility of CSF metabolomics. Additionally, studies in neurodegeneration and neuropsychiatry support the emerging potential of CSF metabolomics to detect neuroinflammation in common CNS diseases such as Alzheimer's disease and depression. We demonstrate metabolites in the tryptophan-kynurenine pathway, nitric oxide pathway, neopterin and major lipid species show moderately consistent ability to differentiate patients with neuroinflammation from controls. Integration of CSF metabolomics into clinical practice is warranted to improve recognition and treatment of neuroinflammation

Production of oriented nitrogen-vacancy color centers in synthetic diamond

The negatively charged nitrogen-vacancy (NV-) center in diamond is an attractive candidate for applications that range from magnetometry to quantum information processing. Here we show that only a fraction of the nitrogen (typically < 0.5 %) incorporated during homoepitaxial diamond growth by Chemical Vapor Deposition (CVD) is in the form of undecorated NV- centers. Furthermore, studies on CVD diamond grown on (110) oriented substrates show a near 100% preferential orientation of NV- centers along only the [111] and [-1-11] directions, rather than the four possible orientations. The results indicate that NV centers grow in as units, as the diamond is deposited, rather than by migration and association of their components. The NV unit of the NVH- is similarly preferentially oriented, but it is not possible to determine whether this defect was formed by H capture at a preferentially aligned NV center or as a complete unit. Reducing the number of NV orientations from 4 orientations to 2 orientations should lead to increased optically-detected magnetic resonance contrast and thus improved magnetic sensitivity in ensemble-based magnetometry.Comment: 13 Pages (inlcuding suplementary information), 4 figure

Schmidt number of pure bi-partite entangled states and methods of its calculation

An entanglement measure for pure-state continuous-variable bi-partite problem, the Schmidt number, is analytically calculated for one simple model of atom-field scattering.Comment: 3 pages, 1 figure; based on the poster presentation reported on the 11th International Conference on Quantum Optics (ICQO'2006, Minsk, May 26 -- 31, 2006), to be published in special issue of Optics and Spectroscop

Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation

Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation. In this work, we studied the DDPM-based segmentation model for 3D multiclass segmentation on two large multiclass data sets (prostate MR and abdominal CT). We observed that the difference between training and test methods led to inferior performance for existing DDPM methods. To mitigate the inconsistency, we proposed a recycling method which generated corrupted masks based on the model's prediction at a previous time step instead of using ground truth. The proposed method achieved statistically significantly improved performance compared to existing DDPMs, independent of a number of other techniques for reducing train-test discrepancy, including performing mask prediction, using Dice loss, and reducing the number of diffusion time steps during training. The performance of diffusion models was also competitive and visually similar to non-diffusion-based U-net, within the same compute budget. The JAX-based diffusion framework has been released at https://github.com/mathpluscode/ImgX-DiffSeg.Comment: Accepted at Deep Generative Models workshop at MICCAI 202

Cerebrospinal fluid metabolites in tryptophan-kynurenine and nitric oxide pathways: biomarkers for acute neuroinflammation.

Aim To explore the cerebrospinal fluid (CSF) metabolite features in acute neuroinflammatory diseases and identify potential biomarkers to diagnose and monitor neuroinflammation. Method A cohort of 14 patients (five females, nine males; mean [median] age 7y 9mo [9y], range 6mo–13y) with acute encephalitis (acute disseminated encephalomyelitis n=6, unknown suspected viral encephalitis n=3, enteroviral encephalitis n=2, seronegative autoimmune encephalitis n=2, herpes simplex encephalitis n=1) and age-matched non-inflammatory neurological disease controls (n=14) were investigated using an untargeted metabolomics approach. CSF metabolites were analyzed with liquid chromatography coupled to high resolution mass spectrometry, followed by subsequent multivariate and univariate statistical methods. Results A total of 35 metabolites could be discriminated statistically between the groups using supervised orthogonal partial least squares discriminant analysis and analysis of variance. The tryptophan-kynurenine pathway contributed nine key metabolites. There was a statistical increase of kynurenine, quinolinic acid, and anthranilic acid in patients with encephalitis, whereas tryptophan, 3-hydroxyanthrnailic acid, and kynurenic acid were decreased. The nitric oxide pathway contributed four metabolites, with elevated asymmetric dimethylarginine and argininosuccinic acid, and decreased arginine and citrulline in patients with encephalitis. An increase in the CSF kynurenine/tryptophan ratio (p<0.001), anthranilic acid/3-hydroxyanthranilic acid ratio (p<0.001), asymmetric dimethylarginine/arginine ratio (p<0.001), and neopterin (p<0.001) strongly predicted neuroinflammation. Interpretation The combination of alterations in the tryptophan-kynurenine pathway, nitric oxide pathway, and neopterin represent a useful potential panel for neuroinflammation and holds potential for clinical translation practice. What this paper adds The kynurenine/tryptophan and anthranilic acid/3-hydroxyanthranilic acid ratios hold great potential as biomarkers of neuroinflammation. Elevation of the asymmetric dimethylarginine/arginine ratio in acute brain inflammation shows dysregulation of the nitric oxide pathway

A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models

Denoising diffusion models have found applications in image segmentation by generating segmented masks conditioned on images. Existing studies predominantly focus on adjusting model architecture or improving inference, such as test-time sampling strategies. In this work, we focus on improving the training strategy and propose a novel recycling method. During each training step, a segmentation mask is first predicted given an image and a random noise. This predicted mask, which replaces the conventional ground truth mask, is used for denoising task during training. This approach can be interpreted as aligning the training strategy with inference by eliminating the dependence on ground truth masks for generating noisy samples. Our proposed method significantly outperforms standard diffusion training, self-conditioning, and existing recycling strategies across multiple medical imaging data sets: muscle ultrasound, abdominal CT, prostate MR, and brain MR. This holds for two widely adopted sampling strategies: denoising diffusion probabilistic model and denoising diffusion implicit model. Importantly, existing diffusion models often display a declining or unstable performance during inference, whereas our novel recycling consistently enhances or maintains performance. We show that, under a fair comparison with the same network architectures and computing budget, the proposed recycling-based diffusion models achieved on-par performance with non-diffusion-based supervised training. By ensembling the proposed diffusion and the non-diffusion models, significant improvements to the non-diffusion models have been observed across all applications, demonstrating the value of this novel training method. This paper summarizes these quantitative results and discusses their values, with a fully reproducible JAX-based implementation, released at https://github.com/mathpluscode/ImgX-DiffSeg.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:01

Effects of the Lattice Discreteness on a Soliton in the Su-Schrieffer-Heeger Model

In this paper we analytically study the effects of the lattice discreteness on the electron band in the SSH model. We propose a modified version of the TLM model which is derived from the SSH model using a continuum approximation. When a soliton is induced in the electron-lattice system, the electron scattering states both at the bottom of the valence band and the top of the conduction band are attracted to the soliton. This attractive force induces weakly localized electronic states at the band edges. Using the modified version of the TLM model, we have succeeded in obtaining analytical solutions of the weakly localized states and the extended states near the bottom of the valence band and the top of the conduction band. This band structure does not modify the order parameters. Our result coincides well with numerical simulation works.Comment: to be appear in J.Phys.Soc.Jpn. Figures should be requested to the author. They will be sent by the conventional airmai

Remarks on hard Lefschetz conjectures on Chow groups

We propose two conjectures of Hard Lefschetz type on Chow groups and prove them for some special cases. For abelian varieties, we shall show they are equivalent to well-known conjectures of Beauville and Murre.Comment: to appear in Sciences in China, Ser. A Mathematic

A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite size pencil beam (FSPB) algorithm with a 3D-density correction method on GPU. This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework [Gu et al. Phys. Med. Biol. 54 6287-97, 2009]. Dosimetric evaluations against Monte Carlo dose calculations are conducted on 10 IMRT treatment plans (5 head-and-neck cases and 5 lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 second (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (~5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning