A 100-m-Scale Modeling Study of a Gale Event on the Lee Side of a Long Narrow Mountain

In this study, a gale event that occurred on the lee side of a long narrow mountain was investigated, together with the associated mountain flows, using a realistic-case large-eddy simulation (LES) that is based on the Weather Research and Forecasting Model. The mountain is located on the southeastern Tibetan Plateau, where approximately 58 gales occur annually, mostly in the afternoons during the winter season. Benefitting from realistic topography and high horizontal resolution as fine as 111 m, the LES can replicate features similar to the wind fields observed during the gale period. Investigation of the early morning wind structure over the mountain revealed that weak inflows were blocked, reversed, and divided in the upstream area and that some weak lee waves, rotors, and two clear lee vortices were evident downstream. As the upstream wind accelerated and the boundary layer developed during the daytime, the lee waves became amplified with severe downslope wind and rotors. The interaction and coherent structure of the downslope wind, rotor, and vortices were investigated to show the severe wind distribution. The mountain drags associated with blocking and amplified lee waves are displayed to show the potential impact on the large-scale model. The linear lee-wave theory was adopted to explain the wave evolution during this event together with a discussion of the uncertainty around low-level nonlinear processes

A Perplexing Case of a DUOX2 Mutation and Graves’ Disease

It is commonly accepted that DUOX2 mutations may cause congenital hypothyroidism and thyrotropin resistance, thus its combination with Graves' disease would be unusual. In this case, our patient's serum thyroid function tests suggested a high probability of thyroid hormone resistance syndrome, but genetic testing did not suggest gene mutations of THRα or THRβ. This is a rare case report of thyroid hormone resistance

Local simulations of MRI turbulence with meshless methods

The magneto-rotational instability (MRI) is one of the most important processes in sufficiently ionized astrophysical disks. Grid-based simulations, especially those using the local shearing box approximation, provide a powerful tool to study the ensuing nonlinear turbulence. On the other hand, while meshless methods have been widely used in both cosmology, galactic dynamics, and planet formation they have not been fully deployed on the MRI problem. We present local unstratified and vertically stratified MRI simulations with two meshless MHD schemes: a recent implementation of SPH MHD (Price2012), and a MFM MHD scheme with a constrained gradient divergence cleaning scheme, as implemented in the GIZMO code \citep{Hopkins2017}. Concerning variants of the SPH hydro force formulation we consider both the "vanilla" SPH and the PSPH variant included in GIZMO. We find, as expected, that the numerical noise inherent in these schemes affects turbulence significantly. A high order kernel, free of the pairing instability, is necessary. Both schemes can adequately simulate MRI turbulence in unstratified shearing boxes with net vertical flux. The turbulence, however, dies out in zero-net-flux unstratified boxes, probably due to excessive and numerical dissipation. In zero-net-flux vertically stratified simulations, MFM can reproduce the MRI dynamo and its characteristic butterfly diagram for several tens of orbits before ultimately decaying. In contrast, extremely strong toroidal fields, as opposed to sustained turbulence, develop in equivalent simulations using SPH MHD. This unphysical state in SPH MHD is likely caused by a combination of excessive artificial viscosity, numerical resistivity, and the relatively large residual errors in the divergence of the magnetic field remaining even after cleaning procedures are applied.Comment: This version has been accepted by ApJ

A multidimensional platform for the purification of non-coding RNA species

A renewed interest in non-coding RNA (ncRNA) has led to the discovery of novel RNA species and post-transcriptional ribonucleoside modifications, and an emerging appreciation for the role of ncRNA in RNA epigenetics. Although much can be learned by amplification-based analysis of ncRNA sequence and quantity, there is a significant need for direct analysis of RNA, which has led to numerous methods for purification of specific ncRNA molecules. However, no single method allows purification of the full range of cellular ncRNA species. To this end, we developed a multidimensional chromatographic platform to resolve, isolate and quantify all canonical ncRNAs in a single sample of cells or tissue, as well as novel ncRNA species. The applicability of the platform is demonstrated in analyses of ncRNA from bacteria, human cells and plasmodium-infected reticulocytes, as well as a viral RNA genome. Among the many potential applications of this platform are a system-level analysis of the dozens of modified ribonucleosides in ncRNA, characterization of novel long ncRNA species, enhanced detection of rare transcript variants and analysis of viral genomes.Singapore-MIT Alliance for Research and TechnologyNational Institute of Environmental Health Sciences (ES017010)National Institute of Environmental Health Sciences (ES002109

Local simulations of MRI turbulence with meshless methods

The magneto-rotational instability (MRI) is one of the most important processes in sufficiently ionized astrophysical disks. Grid-based simulations, especially those using the local shearing box approximation, provide a powerful tool to study the nonlinear turbulence the MRI produces. On the other hand, meshless methods have been widely used in cosmology, galactic dynamics, and planet formation, but have not been fully deployed on the MRI problem. We present local unstratified and vertically stratified MRI simulations with two meshless MHD schemes: a recent implementation of smoothed-particle magnetohydrodynamics (SPH MHD), and a meshless finite-mass (MFM) MHD scheme with constrained gradient divergence cleaning, as implemented in the GIZMO code. Concerning variants of the SPH hydro force formulation, we consider both the "vanilla" SPH and the PSPH variant included in GIZMO. We find, as expected, that the numerical noise inherent in these schemes significantly affects turbulence. Furthermore, a high-order kernel, free of the pairing instability, is necessary. Both schemes adequately simulate MRI turbulence in unstratified shearing boxes with net vertical flux. The turbulence, however, dies out in zero-net-flux unstratified boxes, probably due to excessive numerical dissipation. In zero-net-flux vertically stratified simulations, MFM can reproduce the MRI dynamo and its characteristic butterfly diagram for several tens of orbits before ultimately decaying. In contrast, extremely strong toroidal fields, as opposed to sustained turbulence, develop in equivalent simulations using SPH MHD. The latter unphysical state is likely caused by a combination of excessive artificial viscosity, numerical resistivity, and the relatively large residual errors in the divergence of the magnetic field

A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET

PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers. METHODS: Brain [(18)F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [(18)F]FDG PET images of 45 patients scanned with three different scanners, [(18)F]FET PET images of 18 patients scanned with two different scanners, as well as [(18)F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting. RESULTS: The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = −0.71, p < 0.05) and normalized dose acquisition (r = −0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05). CONCLUSION: The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-021-05644-1

Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer.Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential.Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups.Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer