Etching characteristics of tungsten (W), tungsten silicide (W3Si2), and tungsten oxide (WO3) by energetic trifluorocarbon (CF3+) and argon (Ar+) ion irradiation were examined with the use of mass-selected mono-energetic ion beams in an energy range between 500 and 4000 eV. It is found that the etching yield of W by CF3+ ion irradiation is notably higher than that by Ar+ ion irradiation when their ion energies are the same and sufficiently high, probably because of the formation of volatile tungsten fluoride (WFx) species in the case of CF3+ ion irradiation. Similarly, the etching yield of WO3 by CF3+ ion irradiation is also found to be much higher than that by Ar+ ion irradiation. In contrast, the etching yields of W3Si2 by CF3+ and Ar+ ion irradiation are found to be similar. On the other hand, the etch rates, i.e., the etched depths per unit time of W and W3Si2 by CF3+ or Ar+ ion irradiation, are found to be substantially lower than those of WO3. The results suggest that W and W3Si2 may well serve as hard mask materials for fluorocarbon-based plasma etching processes.

The VO classification scheme tested in this study demonstrated high sensitivity (100%), indicating its robustness in avoiding false negatives and ensuring food safety. Of the carcasses that could have undergone unnecessary condemnation, 22.2% were excluded. The results indicate that the VO classification scheme is recommended as a measure to reduce unnecessary total condemnation induced by VO.

Onion (Allium cepa L.) is an economically valuable crop, but its large, repeat-enriched genome makes genome assembly difficult and limits molecular breeding and biological studies. Herein, we present a chromosomal-level reference genome assembly of the double-haploid onion line DHW30006, constructed by combining PacBio, Illumina, and Hi-C sequencing approaches. The assembled genome totaled 12.77 Gb, with 65,730 gene models, and was anchored to eight pseudo-chromosomes covering 12.07 Gb (94.5%), with a scaffold N50 of 1.40 Gb. DHW30006 onion genome contained improved gene models covering approximately 580 Mb (4.54%) of the genic regions with an average gene length of 8,827 bp and 5.48 exons per gene. These gene models represented the most improved annotation among Allium genomes. This onion genome will serve as a valuable resource for breeding and biological research in Allium plants.

Genotoxic nitrosamine impurities have increasingly posed substantial safety concerns for pharmaceutical formulations, particularly sitagliptin/metformin combination tablets. In particular, <i>N</i>-nitrosodimethylamine (NDMA) and 7-<i>N</i>-nitroso-3-(trifluoromethyl)-5,6,7,8-tetrahydro[1,2,4]triazolo[4,3-<i>a</i>]pyrazine (NTTP)-a newly identified nitrosamine drug-substance-related impurity (<i>N</i>-nitroso-sitagliptin or <i>N</i>-nitroso-STG-19)-have been detected in these pharmaceutical formulations. However, there are no established analytical methods addressing the simultaneous quantification of these two distinct classes of nitrosamine impurities in a single analysis. Therefore, we aimed to develop and validate a robust analytical approach utilising liquid chromatography-mass spectrometry (LC-MS) coupled with atmospheric-pressure chemical ionisation (APCI). Comparison of the ionisation modes and mass analysers revealed that LC-APCI-MS significantly outperformed electrospray ionisation for NDMA, whereas both ionisation methods were suitable for NTTP. Furthermore, both high-resolution quadrupole time-of-flight and triple quadrupole mass spectrometry platforms met the regulatory validation criteria, with excellent linearity (<i>R</i>² > 0.997), recovery (95-102%), and precision (RSD < 5.7%) observed across both instruments. In contrast, GC-MS analysis, one of the major methods for NDMA or nitrosamine detection, was unsuitable for NTTP quantification because of inadequate volatility and chromatographic resolution. As sitagliptin/metformin remains the primary therapy for type 2 diabetes, the proposed method offers a practical solution for quality control monitoring and ensures a safe and stable supply of this widely used combination product.

Motivation: Traditional methods, such as blood volume fraction and vessel size index, often obscure variations in vessel size distributions within imaging voxels. Goal(s): This study introduces a novel technique for assessing microvascular structure changes through vessel size distribution imaging (VDI) using decay due to diffusion in the internal field (DDIF). Approach: Monte Carlo simulations demonstrate the capability of VDI to differentiate distinct vessel distributions. In vivo experiments validate this approach, showing sensitivity to vascular changes following acetazolamide administration and in ischemic stroke models. Results: These findings underscore VDI's potential for microvascular disease diagnosis and monitoring, warranting further exploration in various pathological contexts. Impact: VDI not only validate the effectiveness of the DDIF approach for assessing microvascular dynamics but also highlight its potential application in early disease diagnosis and monitoring.

The increasing demand for programming education and growing class sizes require immediate and personalized feedback. However, integrating Large Language Models (LLMs) like ChatGPT in introductory programming courses raises concerns about AI-assisted cheating. In large-scale settings, faulty code submissions may lead LLMs to overanalyze, causing unnecessary token consumption. This paper proposes a GPT-4o-based code review system that provides accurate feedback while reducing token usage and preventing AI-assisted cheating. Unlike general-purpose LLM tools for professionals, the system is pedagogically designed for primary and secondary students by focusing on review necessity and learner-friendly feedback. The system features a Code Review Module (CRM) that reduces token usage via a Review Necessity Chain (RNC), and Code Correctness Check Module (CCM) combining test case validation with LLM-based assessment. To prevent AI-assisted cheating, the system provides automated feedback on submitted code without prompting and revealing correct answers, which are accessed only through the "Ask Code Tutor" button. In usability test, the system detected up to 42.86% more errors than a conventional online judge. BERTScore analysis showed that over 80% of the system-generated reviews were semantically aligned with human feedback. A performance comparison with state-of-the-art systems demonstrated a blocking success rate of 86%, with a comparable review omission rate. These results indicate that the system provides more accurate feedback than conventional automated code reviews, while achieving token efficiency and supporting self-directed learning through educational feedback. Thus, it can serve as a practical solution for scalable programming education in primary and secondary classes.