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[This corrects the article DOI: 10.3389/fpls.2022.819822.].

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To reveal the connections between the 2024 moment magnitude (Mw) 7.5 Noto earthquake in Japan and the seismicity swarms that preceded it, we investigated its rupture process through near-source waveform analysis and source imaging techniques, combining seismic and geodetic datasets. We found notable complexity in the initial rupture stages. A strong fault asperity, which remained unbroken in preceding seismic swarms, slowed down the rupture. Then, a second rupture initiated at the opposite edge of the asperity, and the asperity succumbed to double-pincer rupture fronts. The failure of this high-stress drop asperity drove the earthquake into a large-scale event. Our observations help unravel the crucial role of fault asperities in controlling swarm migration and rupture propagation and underscore the need for detailed seismological and interdisciplinary studies to assess seismic risk in swarm-prone regions.

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OBJECTIVES: Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM. METHODS: A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC). RESULTS: The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model. CONCLUSIONS: The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans. CRITICAL RELEVANCE STATEMENT: The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans. KEY POINTS: Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.

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BACKGROUND: Lonicerae japonicae flos, also known as Jinyinhua (JYH), is an important component of traditional Chinese patent medicine (TCPM) products. However, the potential for adulteration and substitution with low-quality materials highlights the need for a reliable and sensitive approach to identify the species composition of TCPM products for consumer safety. METHODS AND RESULTS: We used universal ITS2 primers to amplify TCPMs containing JYH. However, the results were inconclusive, as only one operational taxonomic unit (OTU) was identified as Lonicera sp., which could not be identified at the species level. To confirm the species identification of Lonicera sp. in TCPM, we developed a short mini-barcode primer based on the psbA-trnH region, which, in combination with DNA metabarcoding technology, allowed for qualitative and quantitative analysis of artificially mixed samples. We applied the mini-barcode to distinguish TCPMs containing JYH and demonstrated its relatively accurate quantitative ability in identifying two Lonicera species. CONCLUSIONS: Our study presents a method for qualitative and quantitative identification of JYH, providing a promising application of DNA metabarcoding technology in the quality control of TCPM products.

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For wild natural medicine, unanticipated biodiversity as species or varieties with similar morphological characteristics and sympatric distribution may co-exist in a single batch of medical materials, which affects the efficacy and safety of clinical medication. DNA barcoding as an effective species identification tool is limited by its low sample throughput nature. In this study, combining DNA mini-barcode, DNA metabarcoding and species delimitation method, a novel biological sources consistency evaluation strategy was proposed, and high level of interspecific and intraspecific variations were observed and validated among 5376 Amynthas samples from 19 sampling points regarded as "Guang Dilong" and 25 batches of proprietary Chinese medicines. Besides Amynthas aspergillum as the authentic source, 8 other Molecular Operational Taxonomic Units (MOTUs) were elucidated. Significantly, even the subgroups within A. aspergillum revealed here differ significantly on chemical compositions and biological activity. Fortunately, this biodiversity could be controlled when the collection was limited to designated areas, as proved by 2796 "decoction pieces" samples. This batch biological identification method should be introduced as a novel concept regarding natural medicine quality control, and to offer guidelines for in-situ conservation and breeding bases construction of wild natural medicine.

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Mantidis Ootheca (Sangpiaoxiao, mantis egg case) is a typical multi-origin Chinese medicinal material. The Chinese Pharmacopoeia stipulates that the Mantidis Ootheca originates from three species of Mantis: Tenodera sinensis, Statilia maculate, and Hierodula patellifera. However, Mantidis Ootheca mainly relies on field collection, which leads to confusion of its actual origin in the market. As the clinical use of Mantidis Ootheca with unknown original mantis species will pose potential risks to drug safety, it is necessary to survey the commercially available Mantidis Ootheca origin species. However, as the egg case of Mantis, the morphological characters of Mantidis Ootheca are limited and usually cannot serve as accurate identification tool. DNA barcoding, which is widely used in taxonomic studies of animals, is severely affected by the impact of storage pests and DNA degradation. Thus, this study collected a total of 4580 Mantidis Ootheca and pooled separately Mantidis Ootheca samples according to 18 different sources as DNA samples to analyze the origin diversity of Mantidis Ootheca individuals contaminated by common store pests collected in in the market using DNA metabarcoding, and to provide a basis for quality control of Mantidis Ootheca. 37 Mantis ASVs and 9 Mantis MOTUs were identified through species delimitation, and the high-level intraspecific diversity was depicted as haplotype network plot. Besides Tenodera sinensis and Hierodula patellifera as genuine original mantis species defined in the Chinese Pharmacopoeia, Tenodera angustipennis was also the origin species of these Mantidis Ootheca samples.

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Radix Paeoniae Rubra (Chishao), a typical multi-origin Chinese medicinal material, originates from the dried roots of Paeonia lactiflora or P. veitchii. The previous study suggested that these two commonly used Chishao showed variation in their chemical compositions and clinical efficacies. Therefore, accurate identification of different Chishao species was of great significance for the guide of clinical medication, and timely treatment of patients. In this study, the chloroplast genome sequences of P. lactiflora and P. veitchii were obtained by next-generation sequencing (NGS) technology, and then the hypervariable regions were selected to design two mini-barcode candidates for species identification. Combined with DNA metabarcoding technology, we performed qualitative and quantitative analysis on the artificially mixed samples of P. lactiflora and P. veitchii and evaluated the identification ability of these mini-barcode candidates. Furtherly, the mini-barcode with good performance was applied to distinguish the Chinese patent medicine "cerebral thrombosis tablets" containing Chishao. The results indicated that the chloroplast genomes of P. lactiflora and P. veitchii were 152,750 and 152,527 bp, respectively. As published previously, they exhibited a typical quadripartite structure including a large single-copy region (LSC), a small single-copy region (SSC) and a pair of inverted repeat regions (IRs). The nucleotide polymorphism analysis revealed seven variable protein-coding regions as petL, psaI, psbJ, rpl16, ycf1b, psaC, and ndhF, and two mini-barcodes were developed from ycf1b and ndhF respectively. The result suggested that both two mini-barcodes performed well distinguishing P. lactiflora from P. veitchii. Besides, P. lactiflora was the only raw material of Chishao in all collected "cerebral thrombosis tablets" samples. In general, this study has established a method to realize the qualitative and quantitative identification of Chishao as multi-origin Chinese medicinal materials, which can be applied to Chinese patent medicines containing Chishao.