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Abstract Coix lacryma-jobi L. is one of the most economically and medicinally important corns. This study constructed a high-density genetic linkage map of C. lacryma-jobi based on a cross between the parents 'Qianyi No. 2' × 'Wenyi No. 2' and their F2 progeny through high-throughput sequencing and the construction of a specific-locus amplified fragment (SLAF) library. After pre-processing, 325.49 GB of raw data containing 1628 M reads were obtained. A total of 22,944 high-quality SLAFs were identified, among which 3952 SLAFs and 3646 polymorphic markers met the requirements for the construction of a genetic linkage map. The integrated map contained 3605 high-quality SLAFs, which were grouped into ten genetic linkage groups. The total length of the map was 1620.39 cM, with an average distance of 0.45 cM and an average of 360.5 markers per linkage group. This report presents the first high-density genetic map of C. lacryma-jobi. This map was constructed using an F2 population and SLAF-seq approach, which allows the development of a large number of polymorphic markers in a short period. These results provide a platform for precise gene/quantitative trait locus (QTL) mapping, map-based gene separation, and molecular breeding in C. lacryma-jobi. They also help identify a target gene for tracking, splitting quantitative traits, and estimating the phenotypic effects of each QTL for QTL mapping. They are of great significance for improving the efficiency of discovering and utilizing excellent gene resources of C. lacryma-jobi.

Safflower (Carthamus tinctorius L.), a member of the Asteraceae family, is widely used in traditional herbal medicine. This review summarized agronomic conditions, genetic diversity, clinical application, and phytochemicals and pharmacological properties of safflower. The genetic diversity of the plant is rich. Abundant in secondary metabolites like flavonoids, phenols, alkaloids, polysaccharides, fatty acids, polyacetylene, and other bioactive components, the medicinal plant is effective for treating cardiovascular diseases, neurodegenerative diseases, and respiratory diseases. Especially, Hydroxysafflor yellow A (HYSA) has a variety of pharmacological effects. In terms of treatment and prevention of some space sickness in space travel, safflower could be a potential therapeutic agent. Further studies are still required to support the development of safflower in medicine. Our review indicates that safflower is an important medicinal plant and research prospects regarding safflower are very broad and worthy of further investigation.

Rare-earth elements (REEs) are often highly concentrated in sedimentary phosphate deposits, and the microdistribution characteristics and occurrence state of rare earth in these deposits play a crucial role in the overall development and utilization of mineral resources. This study aims to analyze the microdistribution of REEs in REE-bearing phosphate deposits in the Zhijin region of Guizhou at the microstructural level and investigate their occurrence modes. Specifically, rock and mineral identification, X-ray diffraction (XRD), scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM-EDS), and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) were utilized to analyze the samples. The correlation between the distribution of REEs and phosphorus was examined. In addition, the microdistribution of REEs in specific mineral phases and the locations of their occurrence were investigated. The analysis revealed that no REEs existed independently in the deposit. Instead, the distribution of REEs was highly consistent and significantly positively correlated with that of phosphorus. In the microarea structure, REEs were predominantly found both in particles, such as bioclasts, sand debris, and agglomerates, and in phosphate cement, where the main mineral components were collophane and apatite. Conversely, the content of REEs in dolomitized sand debris edges, sparry dolomitic cement, and siliceous cement was considerably lower. Based on these findings, it is speculated that REEs primarily occur within the lattice defects of apatite or on the surface of collophanite. There is a notable contrast in the REE content between the unaltered sand debris at the periphery and the dolomitized sand debris, indicating that the dolomitization in the diagenetic stage resulted in a depletion of REE abundance in the ore. Obviously, the dominant gangue mineral, dolomite, does not serve as the primary host for REEs. Furthermore, the highest concentration of REEs was inside organisms. This finding suggests that the high content of REEs in biological soft tissue may remain under the influence of waves and tides, and REE-bearing apatite may be preferentially separated and fill the cavities of deceased organisms. The second highest content of REEs was found in the shells of organisms, indicating that small shelly organisms absorb phosphorus materials through their life activities to construct their shells, resulting in REE enrichment. Quantitative analysis through sequential extraction procedures displayed that most REEs were present in the residual state, with a smaller portion combined with organic matter. These results confirm that REEs in the Zhijin phosphate deposits primarily exist as isomorphic substitutions in the lattice defects of apatite, with a secondary occurrence as organic matter-bound REEs.

Background: Atrial fibrillation (AF) is a common arrhythmia that can result in adverse cardiovascular outcomes but is often difficult to detect. The use of machine learning (ML) algorithms for detecting AF has become increasingly prevalent in recent years. This study aims to systematically evaluate and summarize the overall diagnostic accuracy of the ML algorithms in detecting AF in electrocardiogram (ECG) signals. Methods: The searched databases included PubMed, Web of Science, Embase, and Google Scholar. The selected studies were subjected to a meta-analysis of diagnostic accuracy to synthesize the sensitivity and specificity. Results: A total of 14 studies were included, and the forest plot of the meta-analysis showed that the pooled sensitivity and specificity were 97% (95% confidence interval [CI]: 0.94–0.99) and 97% (95% CI: 0.95–0.99), respectively. Compared to traditional machine learning (TML) algorithms (sensitivity: 91.5%), deep learning (DL) algorithms (sensitivity: 98.1%) showed superior performance. Using multiple datasets and public datasets alone or in combination demonstrated slightly better performance than using a single dataset and proprietary datasets. Conclusions: ML algorithms are effective for detecting AF from ECGs. DL algorithms, particularly those based on convolutional neural networks (CNN), demonstrate superior performance in AF detection compared to TML algorithms. The integration of ML algorithms can help wearable devices diagnose AF earlier.

Purpose: The aim of this study is to investigate the effects of Al2O3 mass fraction on the corrosive-wear and electrochemical performance of NiTi coating in 3.5% NaCl solution. Design/methodology/approach: The NiTi–xAl2O3 coatings were fabricated on S355 steel by laser cladding, and their corrosive-wear and electrochemical performance were investigated using a wear tester and electrochemical workstation, respectively. Findings: The wear rates of NiTi–5%Al2O3, –10%Al2O3 and –15%Al2O3 coatings are 82.33, 54.23 and 30.10 µm3 mm−1 N−1, respectively, showing that the wear resistance of NiTi–15%Al2O3 coating is the best. The wear mechanism is abrasive wear, which is attributed to the increase of coating hardness by the Al2O3 addition. The polarization resistance of NiTi–5%Al2O3, –10%Al2O3 and –15%Al2O3 coatings is 3,639, 5,125 and 10,024 O cm2, respectively, exhibiting that the NiTi–15% Al2O3 coating has the best corrosion resistance. Originality/value: The roles of Al2O3 in the corrosive-wear and electrochemical performance of NiTi–xAl2O3 coating were revealed through the experimental investigation. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0044/ [ABSTRACT FROM AUTHOR]