Population Structure and Genetic Diversity of Shanlan Landrace Rice for GWAS of Cooking and Eating Quality Traits.

The Shanlan landrace rice in Hainan Province, China, is a unique upland rice germplasm that holds significant value as a genetic resource for rice breeding. However, its genetic diversity and its usefulness in rice breeding have not been fully explored. In this study, a total of eighty-four Shanlan rice, three typical japonica rice cultivars, and three typical indica rice cultivars were subjected to resequencing of their genomes. As a result, 11.2 million high-quality single nucleotide polymorphisms (SNPs) and 1.6 million insertion/deletions (InDels) were detected. Population structure analysis showed all the rice accessions could be divided into three main groups, i.e., Geng/japonica 1 (GJ1), GJ2, and Xian/indica (XI). However, the GJ1 group only had seven accessions including three typical japonica cultivars, indicating that most Shanlan landrace rice are different from the modern japonica rice. Principal component analysis (PCA) showed that the first three principal components explained 60.7% of the genetic variation. Wide genetic diversity in starch physicochemical parameters, such as apparent amylose content (AAC), pasting viscosity, texture properties, thermal properties, and retrogradation representing the cooking and eating quality was also revealed among all accessions. The genome-wide association study (GWAS) for these traits was conducted and identified 32 marker trait associations in the entire population. Notably, the well-known gene Waxy (Wx) was identified for AAC, breakdown viscosity, and gumminess of the gel texture, and SSIIa was identified for percentage of retrogradation and peak gelatinization temperature. Upon further analysis of nucleotide diversity in Wx, six different alleles, wx, Wxa, Wxb, Wxin, Wxla/mw, and Wxlv in Shanlan landrace rice were identified, indicating rich gene resources in Shanlan rice for quality rice breeding. These findings are expected to contribute to the development of new rice with premium quality.

Intelligent identification of fragmented non-magnetic materials for end-of-life refrigerator recycling.

E-waste is a valuable secondary resource containing numerous toxic substances and high-value components. If improperly handled, it will cause severe environmental pollution. Therefore, efficient recycling of this material can reduce environmental pollution. However, after crushing, fine crushing, and magnetic separation, a substantial quantity of fragmented non-magnetic materials with high value, such as copper andg aluminum, remain. Refrigerators, as typical e-waste, have a similar composition to fragmented non-magnetic materials. Consequently, this paper focuses on the issues of low efficiency, environmental pollution, and resource waste in sorting fragmented non-magnetic materials from waste refrigerators. This paper constructs a data set of fragmented non-magnetic materials of refrigerators, augments the data set, and identifies fragmented non-magnetic materials of refrigerators using a computer vision-based deep learning method. In this study, YOLOv5s is used as the benchmark model. The CBAM module is added to the backbone to enable intelligent identification and sorting of fragmented non-magnetic materials in refrigerators. The final identification efficiency of waste refrigerators meets the requirements of industrial applications, with an accuracy rate of 98.3%, a recall rate of 96.8%, and an average accuracy of 98%. Based on the similarity of the composition of e-waste fragmented materials, this model sorting method can be applied to sorting additional e-waste fragmented materials. Furthermore, it provides the theoretical foundation for promoting e-waste resourcefulness.Implications: This paper proposes a recognition model based on YOLOv5s to solve the problems of low sorting efficiency, environmental pollution, harm to health, and resource waste of non-magnetic crushed material from refrigerators. The recognition model principally addresses the following issues: a deep learning model is developed for recognition and sorting to improve e-waste recognition and sorting efficiency. Concerning the issue of environmental benefits in an ecological environment, a vision-based automatic identification method is proposed to sort harmful waste, such as foam, to preserve the ecological environment. In response to the problem of resource waste, this project improves the purity of precious metals, resulting in a recovery rate of 99.1% for copper and 96.44% for aluminum. In other words, the cost of recovering metals has increased. The identification model of non-magnetic crushed material in refrigerators satisfies production identification and sorting requirements. In addition, the method has application and promotion value, sorting a theoretical foundation and method for identifying and classifying e-waste.

Physicochemical characteristics of tea seed starches from twenty-five cultivars.

The physicochemical features of starches separated from tea seeds of 25 cultivars were analyzed. The distinct characteristic of tea seed starches was that they had high apparent amylose content (AAC, 28.94-39.91 %) and resistant starch contents (4.64-8.24 %), suggesting that tea starch can be used for production of low glycemic index food. One cultivar (T12) had smallest breakdown (74.2 RVU) and highest gel hardness, indicating it performed stably during shear thinning, resulting in a firm texture. Another cultivar (T25) had a peak viscosity of 417.6 RVU, a large breakdown and small setback, suggesting a low tendency for retrogradation. There was a range of 61.6 °C to 77.5 °C for the peak gelatinization temperature and 0.163 to 0.390 for the flow behavior index values. These parameters could serve for selecting suitable starches with minor differences in physicochemical properties for food use. Correlation analysis indicated that AAC is a key factor determining starch retrogradation properties. The broad genetic diversity in the tea seed starch physicochemical features provided potentially versatile applications in the food industry. The results gained from the present study contribute to a better understanding of tea seed starch quality, and encourage its application in many value-added food products.

Digital twin-driven prognostics and health management for industrial assets.

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented.

Cutting-edge progress in green technologies for resistant starch type 3 and type 5 preparation: An updated review.

Resistant starch (RS) is a dietary fiber that resists starch hydrolysis in the small intestine, and is fermented in the colon by microorganisms. RS not only has a broad range of benefits in the food and non-food industries but also has a significance impact on health promotion and prevention of non-communicable diseases. RS types 3 and 5 have been the focus of research from an environment-friendly perspective. RS3 is normally formed by recrystallization after physical modification, whereas RS5 is obtained by the complexation of starch and fatty acids through the thermomechanical methods. This review provides updates and approaches to RS3 and RS5 preparations that promote RS content based on green technologies. This information will be useful for future research on RS development and for identifying preparation methods for functional food.

Effects of Artificial Aging on Rice Lysophospholipids.

Lysophospholipids (LPLs) represent a major class of polar lipids crucial for rice's nutritional and functional properties. This study investigates the impact of varying storage temperatures (20, 30, and 40 °C) and humidity (50 and 95%) on the nonstarch and starch LPLs of paddy and milled rice. The findings revealed that the average nonstarch LPL content in paddy rice aged at 20 °C (82.6 µg/g) and 40 °C (83.6 µg/g) was significantly lower than that at 30 °C (95.0 µg/g). The nonstarch LPL content of milled rice aged at 20 °C (78.0 µg/g) was significantly higher than that at 30 and 40 °C. High storage temperature (40 °C) and humidity (95%) resulted in a significant reduction in rice total starch LPC and LPE content when compared to low humidity (50%). The ratio of rice starch/nonstarch LPL components such as LPC16:0 and LPC18:2 remarkably increased with increased storage temperature and humidity.

Cooked Rice Textural Properties and Starch Physicochemical Properties from New Hybrid Rice and Their Parents.

Although great progress has been made in the development of hybrid rice with increased yield, challenges for the improvement of grain quality still remain. In this study, the textural properties of cooked rice and physicochemical characteristics of starch were investigated for 29 new hybrid rice derived from 5 sterile and 11 restorer rice lines. Except for one sterile line Te A (P1) with high apparent amylose content (AAC) (26.9%), all other parents exhibited a low AAC. Gui 263 demonstrated the highest AAC (20.6%) among the restorer lines, so the Te A/Gui 263 hybrid displayed the highest AAC (23.1%) among all the hybrid rice. The mean AAC was similar between sterile, restorer lines and hybrid rice. However, the mean hardness of cooked rice and gels of sterile lines were significantly higher than that of restorer lines and hybrid rice (p < 0.05). Pasting temperature and gelatinization temperatures were significantly higher in the hybrids than in the restorer lines (p < 0.05). Cluster analysis based on the physicochemical properties divided the parents and hybrid rice into two major groups. One group included P1 (Te A), P12 and P14 and three hybrid rice derived from P1, while the other group, including 39 rice varieties, could be further divided into three subgroups. AAC showed significant correlation with many parameters, including peak viscosity, hot peak viscosity, cold peak viscosity, breakdown, setback, onset temperature, peak temperature, conclusion temperature, enthalpy of gelatinization, gel hardness and cooked rice hardness (p < 0.05). Principal component analysis revealed that the first component, comprised of the AAC, peak viscosity, breakdown, setback, onset temperature, peak temperature, conclusion temperature and gel hardness, explained 44.1% of variance, suggesting AAC is the most important factor affecting the grain quality of hybrid rice. Overall, this study enables targeted improvements to key rice grain quality attributes, particularly AAC and textural properties, that will help to develop superior rice varieties.

Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study.

Background: Gastrointestinal stromal tumors (GISTs) represent the most prevalent type of subepithelial lesions (SELs) with malignant potential. Current imaging tools struggle to differentiate GISTs from leiomyomas. This study aimed to create and assess a real-time artificial intelligence (AI) system using endoscopic ultrasonography (EUS) images to differentiate between GISTs and leiomyomas. Methods: The AI system underwent development and evaluation using EUS images from 5 endoscopic centers in China between January 2020 and August 2023. EUS images of 1101 participants with SELs were retrospectively collected for AI system development. A cohort of 241 participants with SELs was recruited for external AI system evaluation. Another cohort of 59 participants with SELs was prospectively enrolled to assess the real-time clinical application of the AI system. The AI system's performance was compared to that of endoscopists. This study is registered with Chictr.org.cn, Number ChiCT2000035787. Findings: The AI system displayed an area under the curve (AUC) of 0.948 (95% CI: 0.921-0.969) for discriminating GISTs and leiomyomas. The AI system's accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) reached 91.7% (95% CI 87.5%-94.6%), 90.3% (95% CI 83.4%-94.5%), 93.0% (95% CI 87.2%-96.3%), 91.9% (95% CI 85.3%-95.7%), and 91.5% (95% CI 85.5%-95.2%), respectively. Moreover, the AI system exhibited excellent performance in diagnosing ≤20 mm SELs (ACC 93.5%, 95% CI 0.900-0.969). In a prospective real-time clinical application trial, the AI system achieved an AUC of 0.865 (95% CI 0.764-0.966) and 0.864 (95% CI 0.762-0.966) for GISTs and leiomyomas diagnosis, respectively, markedly surpassing endoscopists [AUC 0.698 (95% CI 0.562-0.834) for GISTs and AUC 0.695 (95% CI 0.546-0.825) for leiomyomas]. Interpretation: We successfully developed a real-time AI-assisted EUS diagnostic system. The incorporation of the real-time AI system during EUS examinations can assist endoscopists in rapidly and accurately differentiating various types of SELs in clinical practice, facilitating improved diagnostic and therapeutic decision-making. Funding: Science and Technology Commission Foundation of Shanghai Municipality, Science and Technology Commission Foundation of the Xuhui District, the Interdisciplinary Program of Shanghai Jiao Tong University and the Research Funds of Shanghai Sixth people's Hospital.