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Impact of crystal structure symmetry in training datasets on GNN-based energy assessments for chemically disordered CsPbI3
Analysis of the effect of crystal evolution on tool diffusion wear based on the change of cutting parameters
Self-focusing high-frequency ultrasonic transducers for non-destructive testing applications
Preparation of iodine-131 labeled Polyvinyl alcohol-collagen microspheres for radioembolization therapy of liver tumors
Studies on removal of petroleum fractions from spent petrochemical catalysts to prepare them for pyrometallurgical recovery of Ni, Mo and V
Abstract The article presents a study on the removal of petroleum fractions from reprocessed and decommissioned petrochemical catalysts used in refineries. Petrochemical catalysts are hazardous waste due to their content of petroleum fractions (organic fraction), while also containing high concentrations of Ni, Mo and V. The process of removing petroleum fractions from catalysts was carried out by solvent extraction using hexane. Its purpose was to prepare the catalysts for subsequent steps involving pyrometallurgical processes leading to the recovery of Ni, Mo, V. At the same time, the extraction process was aimed at reducing CO2 emissions during melting by deriving the oil fraction before the pyrometallurgical step. The realized studies showed that the degree of removal of the petroleum fraction from catalysts depends on temperature, catalyst/solvent ratio and extraction time. The study showed that it is possible to remove > 40% by weight of the oil fraction initially contained in the catalysts. The research presented in this article is being carried out as part of the LIDER13/0133/2022 project funded by the National Centre for Research and Development, which aims to develop a complete technology for the recovery of Ni, Mo and V from spent petrochemical catalysts.
Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal
Abstract Segmentation of the breast region in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for the automatic measurement of breast density and the quantitative analysis of imaging findings. This study aims to compare various deep learning methods to enhance whole breast segmentation and reduce computational costs as well as environmental effect for future research. We collected fifty-nine DCE-MRI scans from Stavanger University Hospital and, after preprocessing, analyzed fifty-eight scans. The preprocessing steps involved standardizing imaging protocols and resampling slices to ensure consistent volume across all patients. Using our novel approach, we defined new breast boundaries and generated corresponding segmentation masks. We evaluated seven deep learning models for segmentation namely UNet, UNet++, DenseNet, FCNResNet50, FCNResNet101, DeepLabv3ResNet50, and DeepLabv3ResNet101. To ensure robust model validation, we employed 10-fold cross-validation, dividing the dataset into ten subsets, training on nine, and validating on the remaining one, rotating this process to use all subsets for validation. The models demonstrated significant potential across multiple metrics. UNet++ achieved the highest performance in Dice score, while UNet excelled in validation and generalizability. FCNResNet50, notable for its lower carbon footprint and reasonable inference time, emerged as a robust model following UNet++. In boundary detection, both UNet and UNet++ outperformed other models, with DeepLabv3ResNet also delivering competitive results.
Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: molecular docking and dynamics insights
‘Silence is complicity’ — universities must fight the anti-DEI crackdown
10,000-h-stable intermittent alkaline seawater electrolysis
Why women’s brains are more resilient: it could be their ‘silent’ X chromosome
Activation and inhibition mechanisms of a plant helper NLR
Transcriptional adaptation upregulates utrophin in Duchenne muscular dystrophy
Abstract Duchenne muscular dystrophy (DMD) is a muscle-degenerating disease caused by mutations in the DMD gene, which encodes the dystrophin protein1,2. Utrophin (UTRN), the genetic and functional paralogue of DMD, is upregulated in some DMD patients3–5. To further investigate this UTRN upregulation, we first developed an inducible messenger RNA (mRNA) degradation system for DMD by introducing a premature termination codon (PTC) in one of its alternatively spliced exons. Inclusion of the PTC-containing exon triggers DMD mutant mRNA decay and UTRN upregulation. Notably, blocking nonsense-mediated mRNA decay results in the reversal of UTRN upregulation, whereas overexpressing DMD does not. Furthermore, overexpressing DMD PTC minigenes in wild-type cells causes UTRN upregulation, as does a wild-type DMD minigene containing a self-cleaving ribozyme. To place these findings in a therapeutic context, we used splice-switching antisense oligonucleotides (ASOs) to induce the skipping of out-of-frame exons of DMD, aiming to introduce PTCs. We found that these ASOs cause UTRN upregulation. In addition, when using an ASO to restore the DMD reading frame in myotubes derived from a DMD patient, an actual DMD treatment, UTRN upregulation was reduced. Altogether, these results indicate that an mRNA decay-based mechanism called transcriptional adaptation6–8 plays a key role in UTRN upregulation in DMD patients, and they highlight an unexplored therapeutic application of ASOs, as well as ribozymes, in inducing genetic compensation via transcriptional adaptation.