Sweetpotato (Ipomoea batatas) is a genetically complex allohexaploid and a major source of calories and micronutrients in many stress-prone regions; however, it has historically lagged behind model crops in terms of genomic and molecular resources. This review synthesizes recent advances in sweetpotato proteomics, encompassing genome-enabled proteogenomics, stress physiology, storage-root development, metabolism, and postharvest biology. The advent of haplotype-resolved reference genomes, improved genome annotations, and optimized protein extraction protocols has enabled high-coverage LC–MS/MS–based workflows, including tandem mass tag–based quantification and data-independent acquisition. Together, these advances have transformed static protein catalogs into dynamic, systems-level network maps. Key discoveries include the proteomic “lignin–starch switch” underlying storage-root formation, the dual roles of sporamin and β-amylase in both storage and stress responses, and conserved stress-signaling modules that mediate cross-tolerance to heat, drought, cold, and pathogen or nematode attack. Despite these advances, several challenges remain. Matrix effects in phenolic- and starch-rich tissues, limited datasets on post-translational modifications, and the lack of single-cell and subcellular proteomic resolution collectively constrain functional interpretation and the translation of proteomic insights into breeding applications. Future priorities include the proteogenomic refinement of gene models, signaling studies centered on post-translational modifications, organelle- and tissue-resolved proteomics, and the integration of proteomics with multi-omics datasets and quantitative genetics. Such efforts will facilitate the identification of proteomic biomarkers for precision breeding in sweetpotato.
High-entropy alloys (HEAs) are multicomponent systems that have attracted significant attention due to their superior mechanical properties as compared to conventional alloys. Among these properties, hardness plays a vital role and is strongly influenced by the selection and concentration of principal alloying elements. However, predicting the hardness of HEAs is challenging due to the complex and nonlinear relationship between composition and mechanical behavior. In this study, an artificial neural network (ANN) model was developed using experimentally reported hardness data for HEAs composed of Fe, Co, Ni, Cr, V, Mn, Al, Nb, and Cu. The model achieved high prediction accuracy, with adjusted R2 values of 0.9592 and 0.9023 for the training and testing datasets, respectively. A user-friendly graphical interface was also developed to support the practical application of the model. The model was further employed to evaluate the effect of individual alloying elements on hardness using the Index of Relative Importance (IRI). Results showed that Al had the highest positive influence on hardness, while Fe exhibited the most negative impact. Elements such as Al, Cr, Nb and V were found to enhance hardness, whereas Co, Cu, Mn, Ni, and Fe tended to reduce it. Finally, the developed model proposed HEA compositions 30Co–10.5Ni–21.1Cr–7Mn–25Al and 16Fe–27.54Co–47.1Cr–6Mn–13.65Nb with a predicted hardness of 733.67HV and 963.8HV, respectively. The predicted hardness was found near to experimental values.
Abstract Solar activity is usually characterized by different indices from different perspectives, which are commonly interconnected and contain nonstationary oscillations (NSOs). Given the significant influence of solar activity on space weather, the Earth and humans, it is of great significance to predict solar activity for better understanding not only solar dynamics but also geomagnetic activity and space weather. Because of the time‐varying phase and modulus of NSOs, accurate prediction of long‐term solar activity is a challenging task. For this task, we propose an ensemble multivariate multiscale framework taking advantage of the fast and adaptive multivariate empirical mode decomposition, ensemble multivariate nonstationary oscillation resampling, and the time lag effect. We first simultaneously obtain the decomposed components at aligned scales. At each scale, we iteratively predict the target component by integrating its connection with relevant factors, considering error propagation in the iterative prediction and the time lag due to delivery of the impact of specific factors to the target series. Then we sum the predictions at all scales to produce the final long‐term prediction. A simulation experiment using the Rössler system is performed to evaluate and verify the effectiveness of our proposed framework. The method is then applied to produce long‐term predictions of sunspot number and solar flux, which are two important indices of solar activity. The results indicate our proposed framework has similar prediction performance with longer lead time relative to the official model. These numerical results support the feasibility and applicability of the framework in empirical data analysis.
The phytocystatins (PhyCYSs) of plants are members of the cystatin superfamily of proteins, which function as potent inhibitors of cysteine proteases. Arabidopsis PhyCYS2 (AtCYS2) is involved in various biological processes, including protein turnover, development and stress responses. However, the molecular mechanisms of AtCYS2 expression under abiotic stresses remain obscure. Here, we demonstrate that AtCYS2 transcript levels and AtCYS2 promoter-driven β-glucuronidase (PAtCYS2::GUS) activity are significantly induced by exogenous abscisic acid (ABA) as well as by drought, osmotic, and salt stress. Histochemical analysis of PAtCYS2::GUS plants confirmed strong induction in leaves, particularly in guard cells, and in root tips following ABA and abiotic stress treatments. We further identified that the transcription factor ELONGATED HYPOCOTYL 5 (HY5) regulates AtCYS2 expression by directly binding to its promoter. Transient overexpression of HY5 with the PAtCYS2::GUS reporter in Arabidopsis protoplasts revealed that HY5 suppresses AtCYS2 expression under ABA treatment. Consistent with its regulation through ABA signaling, AtCYS2 expression was severely compromised in ABA-insensitive (abi) mutants but was markedly upregulated in the hy5 null mutant. Collectively, these results indicate that while ABA signaling triggers AtCYS2 induction, HY5 acts as a transcriptional repressor to fine-tune this response. This negative feedback loop likely prevents excessive protease inhibition, ensuring optimal adaptation to adverse environments.
Traditional clinical risk models, such as Revised Cardiac Risk Index (RCRI), have limited predictive value for estimating postoperative cardiovascular complications following non-cardiac surgery. This analysis aimed to evaluate prognostic value of thrombogenicity profiles and coronary anatomy for cardiovascular events in patients undergoing non-cardiac surgery. In a prospective cohort of 120 patients who underwent intermediate-to-high risk surgery, thrombogenicity profiles were assessed using thromboelastography (TEG®) and conventional hemostatic measurements before surgery. Coronary artery disease (CAD) was preoperatively defined as presence of significant stenosis (≥ 50 http://www.clinicaltrials.gov . Unique identifier: NCT02250963. Sequential integration of thrombogenicity profiles and coronary anatomy assessed by CCTA improves perioperative cardiovascular risk prediction beyond clinical risk stratification alone in patients undergoing non-cardiac surgery. CAD = coronary artery disease; CCTA = coronary computed tomography angiography; CI = confidence interval; CV = cardiovascular; MI = myocardial infarction; MINS = myocardial injury in non-cardiac surgery; PFCS = platelet–fibrin clot strength; RCRI = Revised Cardiac Risk Index; TEG® = thromboelastography.
