Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks.

BACKGROUND: Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE: To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS: Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS: The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION: CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

Efficient Synthesis of Dimethyl Ether from Methanol in a Bifunctional Zeolite Membrane Reactor.

A sandwich FAU-LTA zeolite dual-layer membrane has been developed and used as a catalytic membrane reactor for the synthesis of dimethyl ether (DME). In the top H-FAU layer with mild acidity, methanol is dehydrated to DME. The other reaction product, water, is removed in situ through a hydrophilic Na-LTA layer, which is located between the porous alumina support and the H-FAU top layer. The combination of mild acidity with the continuous removal of water results in high methanol conversion (90.9 % at 310 °C) and essentially 100 % DME selectivity. Furthermore, owing to the selective and continuous removal of water through the Na-LTA membrane, catalyst deactivation can be effectively suppressed.

VCFshiny: an R/Shiny application for interactively analyzing and visualizing genetic variants.

Summary: Next-generation sequencing generates variants that are typically documented in variant call format (VCF) files. However, comprehensively examining variant information from VCF files can pose a significant challenge for researchers lacking bioinformatics and programming expertise. To address this issue, we introduce VCFshiny, an R package that features a user-friendly web interface enabling interactive annotation, interpretation, and visualization of variant information stored in VCF files. VCFshiny offers two annotation methods, Annovar and VariantAnnotation, to add annotations such as genes or functional impact. Annotated VCF files are deemed acceptable inputs for the purpose of summarizing and visualizing variant information. This includes the total number of variants, overlaps across sample replicates, base alterations of single nucleotides, length distributions of insertions and deletions (indels), high-frequency mutated genes, variant distribution in the genome and of genome features, variants in cancer driver genes, and cancer mutational signatures. VCFshiny serves to enhance the intelligibility of VCF files by offering an interactive web interface for analysis and visualization. Availability and implementation: The source code is available under an MIT open source license at https://github.com/123xiaochen/VCFshiny with documentation at https://123xiaochen.github.io/VCFshiny.

Looking for the Genes Related to Lung Cancer From Nasal Epithelial Cells by Network and Pathway Analysis.

Previous studies have indicated that the airway epithelia of lung cancer-associated injury can extend to the nose and it was associated with abnormal gene expression. The aim of this study was to find the possible lung cancer-related genes from the nasal epithelium as bio-markers for lung cancer detection. WGCNA was performed to calculate the module-trait correlations of lung cancer based on the public microarray dataset, and their data were processed by statistics of RMA and t-test. Four specific modules associated with clinical features of lung cancer were constructed, including blue, brown, yellow, and light blue. Of which blue or brown module showed strong connection to genetic connectivity. From the brown module, it was found that HCK, NCF1, TLR8, EMR3, CSF2RB, and DYSF are the hub genes, and from the blue module, it was found that SPEF2, ANKFN1, HYDIN, DNAH5, C12orf55, and CCDC113 are the pivotal genes corresponding to the grade. These genes can be taken as the bio-markers to develop a noninvasive method of diagnosing early lung cancer.

Combined dehydrogenation of glycerol with catalytic transfer hydrogenation of H2 acceptors to chemicals: Opportunities and challenges.

Catalytic transformation of low-cost glycerol to value-added lactic acid (LA) is considered as one of the most promising technologies for the upgradation of glycerol into renewable products. Currently, research studies reveal that anaerobic transformation of glycerol to LA could also obtain green H2 with the same yield of LA. However, the combined value-added utilization of released H2 with high selectivity of LA during glycerol conversion under mild conditions still remains a grand challenge. In this perspective, for the first time, we conducted a comprehensive and critical discussion on current strategies for combined one-pot/tandem dehydrogenation of glycerol to LA with catalytic transfer hydrogenation of H2 acceptors (such as CO2) to other chemicals. The aim of this overview was to provide a general guidance on the atomic economic reaction pathway for upgrading low-cost glycerol and CO2 to LA as well as other chemicals.