Understanding starch biosynthesis in potatoes for metabolic engineering to improve starch quality: A detailed review.

Potato tubers accumulate substantial quantities of starch, which serves as their primary energy reserve. As the predominant component of potato tubers, starch strongly influences tuber yield, processing quality, and nutritional attributes. Potato starch is distinguished from other food starches by its unique granule morphology and compositional attributes. It possesses large, oval granules with amylose content ranging from 20 to 33 % and high phosphorus levels, which collectively determine the unique physicochemical characteristics. These physicochemical properties direct the utility of potato starch across diverse food and industrial applications. This review synthesizes current knowledge on the molecular factors controlling potato starch biosynthesis and structure-function relationships. Key topics covered are starch granule morphology, the roles and regulation of major biosynthetic enzymes, transcriptional and hormonal control, genetic engineering strategies, and opportunities to tailor starch functionality. Elucidating the contributions of different enzymes in starch biosynthesis has enabled targeted modification of potato starch composition and properties. However, realizing the full potential of this knowledge faces challenges in optimizing starch quality without compromising plant vigor and yield. Overall, integrating multi-omics datasets with advanced genetic and metabolic engineering tools can facilitate the development of elite cultivars with enhanced starch yield and tailored functionalities.

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.

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.

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.

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.

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.

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.

Molecular Research for Cereal Grain Quality.

Cereals such as wheat (Triticum aestivum L [...].

Applying artificial intelligence algorithm in the design of a guide plate for mandibular angle ostectomy.

PURPOSE: Surgical guide plates can improve the accuracy of surgery, although their design process is complex and time-consuming. This study aimed to use artificial intelligence (AI) to design standardized mandibular angle ostectomy guide plates and reduce clinician workload. METHODS: An intelligence algorithm was designed and trained to design guide plates, with a safety-ensuring penalty factor added. A single-center retrospective cohort study was conducted to test the algorithm among patients who had visited our hospital between 2020 and 2021 for mandibular angle ostectomy. We included patients diagnosed with mandibular angle hypertrophy and excluded those combined with other facial malformations. The guide plate design method acted as the primary predictor, which was AI algorithm vs. experienced residents. Moreover, the symmetry of plate-guided ostectomy was chosen as the primary outcome. The safety, shape, location, effectiveness, and design duration of the guide plate were also recorded. The independent samples t-test and Pearson's chi-squared test were used and P-values < 0.05 were considered significant. RESULTS: Fifty patients (7 men, 43 women; 27 ± 4 years) were included. The two groups differed significantly in terms of safety (7.02 vs. 5.25, P < 0.05) and design duration (24.98 vs. 1685.08, P ＜ 0.05). The ostectomy symmetry and shape, location, and effectiveness of the guide plates did not differ significantly between the two groups. CONCLUSIONS: The intelligent algorithm can improve safety and save time for guide plate design, ensuring other quality of the guide plates. It has good potential applicability in accurate mandibular angle ostectomy.

A digital twin-driven flexible scheduling method in a human-machine collaborative workshop based on hierarchical reinforcement learning.

Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly, but the existing production lines are not flexible and efficient enough to dynamically adapt to market demand. The human-machine collaboration system combines the advantages of humans and machines, and provides feasibility for implementing different manufacturing tasks. With dynamic adjustment of robots and operators in the production line, the flexibility of the human-machine collaborative production line can be further improved. Therefore, a parallel production line is set up as a parallel community, and the digital twin community model of the intelligent workshop is constructed. The fusion and interaction between the production communities enhance the production flexibility of the manufacturing shop. Aiming at the overall production efficiency and load balancing state, a digital twin-driven intra-community process optimization algorithm based on hierarchical reinforcement learning is proposed, and as a key framework to improve the production performance of production communities, which is used to optimize the proportion of human and machine involvement in work. Finally, taking the assembly process of ventilators as an example, it is proved that the intelligent scheduling strategy proposed in this paper shows stronger adjustment ability in response to dynamic demand as well as production line changes.

Correction: Ying et al. Dynamic Change in Starch Biosynthetic Enzymes Complexes during Grain-Filling Stages in BEIIb Active and Deficient Rice. Int. J. Mol. Sci. 2022, 23, 10714.

In the original publication [...].

In vitro fecal fermentation characteristics of mutant rice starch depend more on amylose content than crystalline structure.

To obtain the relation between rice starch features and fermentation characteristics, rice starches with various polymorphic types and apparent amylose contents were subjected to in vitro fecal fermentation. Gas and short-chain fatty acid production was evaluated as a function of fermentation time, and the microbial responses were monitored by 16S rRNA sequencing technique at the end of fermentation. Regardless of polymorphic type, three high-amylose mutant rice starches (i.e., GM03, A-type; BP577, B-type; Wx21TT, C-type) displayed significantly slower fermentation rate during the first 12 h and higher final butyrate yield (17.6-17.9 mM) compared to the A-type normal starches (9311 and Wx22TT), and promoted the proliferation of Roseburia. However, A-type normal rice starches presented higher propionate production, and increased the growth of Bacteroides and Megamonas. The principal component and redundancy analyses indicated that three high-amylose mutant rice starches showed similar abundance and migration of microbial communities, and the apparent amylose content was closely correlated with the abundance of their five key amplicon sequence variants. Our results demonstrated that amylose content might be a controlling factor in determining the fermentation properties of rice starches than crystalline structure.

Identification of a new allele of soluble starch synthase IIIa involved in the elongation of amylopectin long chains in a chalky rice mutant.

A chalky endosperm mutant (GM03) induced from an indica rice GLA4 was used to investigate the functional gene in starch biosynthesis. Bulked segregant analysis and sanger sequencing determined that a novel mutation in soluble starch synthase IIIa (SSIIIa) is responsible for the chalky phenotype in GM03. Complementary test by transforming the active SSIIIa gene driven by its native promoter to GM03 recovered the phenotype to its wildtype. The expression of SSIIIa was significantly decreased, while SSIIIa protein was not detected in GM03. The mutation of SSIIIa led to increased expression of most of starch synthesis related genes and elevated the levels of most of proteins in GM03. The CRISPR/Cas9 technology was used for targeted disruption of SSIIIa, and the mutant lines exhibited chalky endosperm which phenocopied the GM03. Additionally, the starch fine structure in the knockout mutant lines ss3a-1 and ss3a-2 was similar with the GM03, which showed increased amylose content, higher proportions of B1 and B2 chains, much lower proportions of B3 chains and decreased degree of crystallinity, leading to altered thermal properties with lower gelatinization temperature and enthalpy. Collectively, these results suggested that SSIIIa plays an important role in starch synthesis by elongating amylopectin long chains in rice.

Effects of drought stress and elevated CO2 on starch fine structures and functional properties in indica rice.

The changes in rice starch fine structures and functional properties induced by drought, elevated CO2 (eCO2) and combined treatments remain to be elucidated comprehensively. In this study, starch molecular structures were characterized by size-exclusion chromatography and fluorophore-assisted carbohydrate electrophoresis, and the data from amylopectin chain-length distributions was fitted with a mathematical model to obtain parameters for statistical analysis. Starch crystalline structures, thermal and pasting properties of rice flour were also analyzed. All the treatments decreased the seed setting rate and increased chalky grains, reflecting the abnormal starch accumulation. However, eCO2 partially increased the seed setting rate and chalky grains under drought stress. The stress-treated starch samples had shorter amylopectin short and intermediate chains, the lower degree of ordered structures, but similar amylose content and relative crystallinity. This led to lower onset gelatinization temperature and paste viscosities, which may be responsible for the worse sensory quality of rice grains.

Combined Effects of Different Alleles of FLO2, Wx and SSIIa on the Cooking and Eating Quality of Rice.

The improvement of the cooking and eating quality (CEQ) of rice is one of the major objectives of current rice-breeding programs. A few major genes such as Waxy (Wx) and starch synthase IIa (SSIIa) have been successfully applied in molecular breeding. However, their interactive effects on CEQ have not been fully understood. In this study, a recombinant inbred line (RIL) population was constructed by crossing the white-core mutant GM645 with the transparent phenotype of the japonica rice variety Tainung 67 (TN67). GM645 and TN67 contain different alleles of FLOURY ENDOSPERM2 (FLO2), Wx, and SSIIa. The effects of different allele combinations of FLO2, Wx, and SSIIa on the CEQ of rice were investigated. The inbred lines with the mutation allele flo2 had a significantly lower apparent amylose content (AAC), viscosity characteristics except for setback (SB), and gel texture properties compared to those lines with the FLO2 allele. The allelic combination of FLO2 and Wx significantly affected the AAC, breakdown (BD), and gel textural properties, which could explain most of the variations in those rice quality traits that were correlated with AAC. The allelic combination of FLO2 and SSIIa significantly affected the hot paste viscosity (HPV) and pasting temperature (PT). The Wx × SSIIa interaction had a significant effect on the PT. The interaction of FLO2, Wx and SSIIa significantly affected the AAC, cold paste viscosity (CPV), PT, and consistency viscosity (CS). These results highlight the important roles of these quality-related genes in regulating the CEQ of rice and provide new clues for rice-quality improvement by marker-assisted selection.

Recent Advances in Molecular Improvement for Potato Tuber Traits.

Potato is an important crop due to its nutritional value and high yield potential. Improving the quality and quantity of tubers remains one of the most important breeding objectives. Genetic mapping helps to identify suitable markers for use in the molecular breeding, and combined with transgenic approaches provides an efficient way for gaining desirable traits. The advanced plant breeding tools and molecular techniques, e.g., TALENS, CRISPR-Cas9, RNAi, and cisgenesis, have been successfully used to improve the yield and nutritional value of potatoes in an increasing world population scenario. The emerging methods like genome editing tools can avoid incorporating transgene to keep the food more secure. Multiple success cases have been documented in genome editing literature. Recent advances in potato breeding and transgenic approaches to improve tuber quality and quantity have been summarized in this review.

Dynamic Change in Starch Biosynthetic Enzymes Complexes during Grain-Filling Stages in BEIIb Active and Deficient Rice.

Starch is the predominant reserve in rice (Oryza sativa L.) endosperm, which is synthesized by the coordinated efforts of a series of starch biosynthetic-related enzymes in the form of a multiple enzyme complex. Whether the enzyme complex changes during seed development is not fully understood. Here, we investigated the dynamic change in multi-protein complexes in an indica rice variety IR36 (wild type, WT) and its BEIIb-deficient mutant (be2b) at different developmental stages. Gel permeation chromatography (GPC) and Western blotting analysis of soluble protein fractions revealed most of the enzymes except for SSIVb were eluted in smaller molecular weight fractions at the early developing stage and were transferred to higher molecular weight fractions at the later stage in both WT and be2b. Accordingly, protein interactions were enhanced during seed development as demonstrated by co-immunoprecipitation analysis, suggesting that the enzymes were recruited to form larger protein complexes during starch biosynthesis. The converse elution pattern from GPC of SSIVb may be attributed to its vital role in the initiation step of starch synthesis. The number of protein complexes was markedly decreased in be2b at all development stages. Although SSIVb could partially compensate for the role of BEIIb in protein complex formation, it was hard to form a larger protein complex containing over five proteins in be2b. In addition, other proteins such as PPDKA and PPDKB were possibly present in the multi-enzyme complexes by proteomic analyses of high molecular weight fractions separated from GPC. Two putative protein kinases were found to be potentially associated with starch biosynthetic enzymes. Collectively, our findings unraveled a dynamic change in the protein complex during seed development, and potential roles of BEIIb in starch biosynthesis via various protein complex formations, which enables a deeper understanding of the complex mechanism of starch biosynthesis in rice.