Irradiation-Hardening Model of TiZrHfNbMo0.1 Refractory High-Entropy Alloys.

In order to find more excellent structural materials resistant to radiation damage, high-entropy alloys (HEAs) have been developed due to their characteristics of limited point defect diffusion such as lattice distortion and slow diffusion. Specially, refractory high-entropy alloys (RHEAs) that can adapt to a high-temperature environment are badly needed. In this study, TiZrHfNbMo0.1 RHEAs are selected for irradiation and nanoindentation experiments. We combined the mechanistic model for the depth-dependent hardness of ion-irradiated metals and the introduction of the scale factor f to modify the irradiation-hardening model in order to better describe the nanoindentation indentation process in the irradiated layer. Finally, it can be found that, with the increase in irradiation dose, a more serious lattice distortion caused by a higher defect density limits the expansion of the plastic zone.

The improvement of water barrier property in gelatin/carboxymethyl cellulose composite film by electrostatic interaction regulation and its application in strawberry preservation.

Gelatin (GL) and carboxymethyl cellulose (CMC) are common natural components for edible films, but their water barrier performance are finite as hydrophilic polymers. In this study, a GL/CMC water barrier film was prepared, characterized and applied. The microstructure results showed that complex coacervation at pH 2.0 and cross-linking effect of sodium benzoate resulted in strong interaction forces and dense structure of this film. Compared with pure GL or CMC film, this novel composite film decreased water vapor permeability by approximately 90%, and possessed applicable water solubility (51.5%) and stronger barrier to oxygen and UV light. Acidic environment and sodium benzoate endowed antibacterial activity. Furthermore, the water barrier coating film decreased water loss by 47.8% and improved overall quality of fresh strawberries stored at 25 °C for 6 d. Therefore, the novel water barrier film based on complex coacervation and cross-linking is promising to control the postharvest quality of perishable berries.

Wafer Scale Gallium Nitride Integrated Electrode Toward Robust High Temperature Energy Storage.

Gallium Nitride (GaN), as the representative of wide bandgap semiconductors, has great prospects in accomplishing rapid charge delivery under high-temperature environments thanks to excellent structural stability and electron mobility. However, there is still a gap in wafer-scale GaN single-crystal integrated electrodes applied in the energy storage field. Herein, Si-doped GaN nanochannel with gallium oxynitride (GaON) layer on a centimeter scale (denoted by GaN NC) is reported. The Si atoms modulate electronic redistribution to improve conductivity and drive nanochannel formation. Apart from that, the distinctive nanochannel configuration with a GaON layer provides adequate active sites and extraordinary structural stability. The GaN-based supercapacitors are assembled and deliver outstanding charge storage capabilities at 140 °C. Surprisingly, 90% retention is maintained after 50 000 cycles. This study opens the pathway toward wafer-scale GaN single-crystal integrated electrodes with self-powered characteristics that are compatible with various (opto)-electronic devices.

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OBJECTIVES: To investigate the factors influencing the occurrence of small for gestational age (SGA) at different degrees and provide a basis for early identification of severe SGA cases. METHODS: Neonatal and maternal prenatal information were retrospectively collected from January 2018 to December 2022 at Peking University People's Hospital. The neonates were divided into three groups: severe SGA group (birth weight below the 3rd percentile for gestational age and sex), mild SGA group (birth weight ≥3rd percentile and <10th percentile), and non-SGA group (birth weight ≥10th percentile). An ordered multinomial logistic regression model was used to analyze the factors influencing the occurrence of SGA at different degrees. RESULTS: A total of 14 821 neonates were included, including 258 cases (1.74%) in the severe SGA group, 902 cases (6.09%) in the mild SGA group, and 13 661 cases (92.17%) in the non-SGA group. The proportions of preterm births and stillbirths were higher in the severe SGA group compared to the mild SGA and non-SGA groups (P<0.0125). The proportion of neonatal asphyxia was higher in both the severe SGA and mild SGA groups compared to the non-SGA group (P<0.0125). Ordered multinomial logistic regression analysis showed that maternal pre-pregnancy underweight (OR=1.838), maternal pre-pregnancy obesity (OR=3.024), in vitro fertilization-embryo transfer (OR=2.649), preeclampsia (OR=1.743), connective tissue disease during pregnancy (OR=1.795), nuchal cord (OR=1.213), oligohydramnios (OR=1.848), and intrauterine growth restriction (OR=27.691) were all associated with a higher risk of severe SGA (P<0.05). Maternal parity as a multipara (OR=0.457) was associated with a lower likelihood of severe SGA (P<0.05). CONCLUSIONS: Maternal pre-pregnancy underweight, maternal pre-pregnancy obesity, in vitro fertilization-embryo transfer, preeclampsia, connective tissue disease during pregnancy, oligohydramnios, nuchal cord, and intrauterine growth restriction are closely related to the occurrence of more severe SGA. Maternal parity as a multipara acts as a protective factor against the occurrence of severe SGA.

Screening, identification, and mechanism analysis of starch-degrading bacteria during curing process in tobacco leaf.

Tobacco, a vital economic crop, had its quality post-curing significantly influenced by starch content. Nonetheless, the existing process parameters during curing were inadequate to satisfy the starch degradation requirements. Microorganisms exhibit inherent advantages in starch degradation, offering significant potential in the tobacco curing process. Our study concentrated on the microbial populations on the surface of tobacco leaves and in the rhizosphere soil. A strain capable of starch degradation, designated as BS3, was successfully isolated and identified as Bacillus subtilis by phylogenetic tree analysis based on 16SrDNA sequence. The application of BS3 on tobacco significantly enhanced enzyme activity and accelerated starch degradation during the curing process. Furthermore, analyses of the metagenome, transcriptome, and metabolome indicated that the BS3 strain facilitated starch degradation by regulating surface microbiota composition and affecting genes related to starch hydrolyzed protein and key metabolites in tobacco leaves. This study offered new strategies for efficiently improving the quality of tobacco leaves.

Quantitative characterization of micro-scanning imaging aliasing and optical parameter optimization.

Imaging aliasing is a common problem in the imaging domain. The aliasing of micro-scanning imaging is difficult to characterize accurately, and the matching relationship between the optical system and micro-scanning sampling is unclear. In this paper, a micro-scanning aliasing analysis model is proposed based on the property of sampling squeeze, in which the transfer functions of the optical system, detector, and digital filter are coupled with the micro-scanning sampling process. First, the imaging aliasing under different micro-scanning sampling modes is evaluated based on the constraint relationship of the transfer functions for each part. The stretch factor of the transfer function under micro-scanning sampling is calculated by utilizing the amount of aliasing. Second, the micro-scanning imaging transfer function under different optical parameters is predicted by the stretch factor, and the results indicate the existence of an optimal F-number that maximizes the micro-scanning performance improvement. Furthermore, the optimal micro-scanning imaging F-numbers for different fill factors are given, and the matching relationship between optical parameters, fill factors and micro-scanning mode is analyzed. Finally, a micro-scanning imaging simulation is performed based on the actual imaging transfer and micro-scanning sampling process. The simulation experiment verifies the accuracy of the micro-scanning aliasing model and gives the consistent test results of the optimal F-number. This paper can provide theoretical support for the matching relationship among micro-scanning imaging parameters, which is of great significance for the refined optimal design of micro-scanning imaging systems.

The synergistic antibacterial activity and mechanism of colistin-oxethazaine combination against gram-negative pathogens.

Background: The rapid spread of bacteria with plasmid-mediated resistance to antibiotics poses a serious threat to public health. The search for potential compounds that can increase the antibacterial activity of existing antibiotics is a promising strategy for addressing this issue. Methods: Synergistic activity of the FDA-approved agent oxethazine combined with colistin was investigated in vitro using checkerboard assays and time-kill curves. The synergistic mechanisms of their combination of oxethazine and colistin was explored by fluorescent dye, scanning electron microscopy (SEM) and LC-MS/MS. The synergistic efficacy was evaluated in vivo by the Galleria mellonella and mouse sepsis models. Results: In this study, we found that oxethazine could effectively enhance the antibacterial activity of colistin against both mcr-positive and -negative pathogens, and mechanistic assays revealed that oxethazine could improve the ability of colistin to destruct bacterial outer membrane and cytoplasmic membrane permeability. In addition, their combination triggered the accumulation of reactive oxygen species causing additional damage to the membrane structure resulting in cell death. Furthermore, oxethazine significantly enhanced the therapeutic efficacy of colistin in two animal models. Conclusion: These results suggested that oxethazine, as a promising antibiotic adjuvant, can effectively enhance colistin activity, providing a potential strategy for treating multidrug-resistant bacteria.

Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer.

BACKGROUND: Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis. AIM: To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients. METHODS: A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model. RESULTS: The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice. CONCLUSION: This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.

Flexible sensors with zero Poisson's ratio.

Flexible sensors have been developed for the perception of various stimuli. However, complex deformation, usually resulting from forces or strains from multi-axes, can be challenging to measure due to the lack of independent perception of multiaxial stimuli. Herein, flexible sensors based on the metamaterial membrane with zero Poisson's ratio (ZPR) are proposed to achieve independent detection of biaxial stimuli. By deliberately designing the geometric dimensions and arrangement parameters of elements, the Poisson's ratio of an elastomer membrane can be modulated from negative to positive, and the ZPR membrane can maintain a constant transverse dimension under longitudinal stimuli. Due to the accurate monitoring of grasping force by ZPR sensors that are insensitive to curvatures of contact surfaces, rigid robotic manipulators can be guided to safely grasp deformable objects. Meanwhile, the ZPR sensor can also precisely distinguish different states of manipulators. When ZPR sensors are attached to a thermal-actuation soft robot, they can accurately detect the moving distance and direction. This work presents a new strategy for independent biaxial stimuli perception through the design of mechanical metamaterials, and may inspire the future development of advanced flexible sensors for healthcare, human-machine interfaces and robotic tactile sensing.

CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer.

BACKGROUND: The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. OBJECTIVE: This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. METHODS: We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. RESULTS: Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776-0.907) and 0.955 (95% CI: 0.926-0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677-0.948) and 0.893 (95% CI: 0.795-0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature. CONCLUSIONS: Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.

Spin Effect to Regulate the Electronic Structure of Ir─Fe Aerogels for Efficient Acidic Water Oxidation.

"Spin" has been recently reported as an important degree of electronic freedom to promote catalysis, yet how it influences electronic structure remains unexplored. This work reports the spin-induced orbital hybridization in Ir─Fe bimetallic aerogels, where the electronic structure of Ir sites is effectively regulated by tuning the spin property of Fe atoms. The spin-optimized electronic structure boosts oxygen evolution reaction (OER) electrocatalysis in acidic media, resulting in a largely improved catalytic performance with an overpotential of as low as 236 mV at 10 mA cm-2. Furthermore, the gelation kinetics for the aerogel synthesis is improved by an order of magnitude based on the introduction of a magnetic field. Density functional theory calculation reveals that the increased magnetic moment of Fe (3d orbital) changes the d-band structure (i.e., the d-band center and bandwidth) of Ir (5d orbital) via orbital hybridization, resulting in optimized binding of reaction intermediates. This strategy builds the bridge between the electron spin theory with the d-band theory and provides a new way for the design of high-performance electrocatalysts by using spin-induced orbital interaction.

Multilabel Feature Selection via Shared Latent Sublabel Structure and Simultaneous Orthogonal Basis Clustering.

Multilabel feature selection solves the dimension distress of high-dimensional multilabel data by selecting the optimal subset of features. Noisy and incomplete labels of raw multilabel data hinder the acquisition of label-guided information. In existing approaches, mapping the label space to a low-dimensional latent space by semantic decomposition to mitigate label noise is considered an effective strategy. However, the decomposed latent label space contains redundant label information, which misleads the capture of potential label relevance. To eliminate the effect of redundant information on the extraction of latent label correlations, a novel method named SLOFS via shared latent sublabel structure and simultaneous orthogonal basis clustering for multilabel feature selection is proposed. First, a latent orthogonal base structure shared (LOBSS) term is engineered to guide the construction of a redundancy-free latent sublabel space via the separated latent clustering center structure. The LOBSS term simultaneously retains latent sublabel information and latent clustering center structure. Moreover, the structure and relevance information of nonredundant latent sublabels are fully explored. The introduction of graph regularization ensures structural consistency in the data space and latent sublabels, thus helping the feature selection process. SLOFS employs a dynamic sublabel graph to obtain a high-quality sublabel space and uses regularization to constrain label correlations on dynamic sublabel projections. Finally, an effective convergence provable optimization scheme is proposed to solve the SLOFS method. The experimental studies on the 18 datasets demonstrate that the presented method performs consistently better than previous feature selection methods.

Senolytic and senomorphic agent procyanidin C1 alleviates structural and functional decline in the aged retina.

Increased cellular senescence burden contributes in part to age-related organ dysfunction and pathologies. In our study, using mouse models of natural aging, we observed structural and functional decline in the aged retina, which was accompanied by the accumulation of senescent cells and senescence-associated secretory phenotype factors. We further validated the senolytic and senomorphic properties of procyanidin C1 (PCC1) both in vitro and in vivo, the long-term treatment of which ameliorated age-related retinal impairment. Through high-throughput single-cell RNA sequencing (scRNA-seq), we comprehensively characterized the retinal landscape after PCC1 administration and deciphered the molecular basis underlying the senescence burden increment and elimination. By exploring the scRNA-seq database of age-related retinal disorders, we revealed the role of cellular senescence and the therapeutic potential of PCC1 in these pathologies. Overall, these results indicate the therapeutic effects of PCC1 on the aged retina and its potential use for treating age-related retinal disorders.

Rapid screening of acetylcholinesterase active contaminants in water: A solid phase microextraction-based ligand fishing approach.

Effect-directed analysis (EDA) has been increasingly used for screening toxic contaminants in the environment, but conventional EDA procedures are often time-consuming and labor-extensive. This challenges the use of EDA for toxicant identification in the scenarios when quick answers are demanded. Herein, a solid phase microextraction ligand fishing (SPME-LF) strategy has been proposed as a rapid EDA approach for identifying acetylcholinesterase (AChE) active compounds in water. The feasibility of ligand fishing techniques for screening AChE active chemicals from environmental mixtures was first verified by a membrane separation method. Then, SPME fibers were prepared through self-assembly of boronic acid groups with AChE via co-bonding and applied for SPME-LF. As AChE coated SPME fibers selectively enriched AChE-active compounds from water, comparing sorbing compounds by the SPME fibers with and without AChE coating can quickly distinguish AChE toxicants in mixtures. Compared with conventional EDA, SPME-LF does not require repeating sample separations and bioassays, endowing SPME-LF with the merits of low-cost, labor-saving, and user-friendly. It is believed that cost-efficient and easy-to-use SPME-LF strategy can potentially be a rapid EDA method for screening receptor-specific toxicants in aquatic environment, especially applicable in time-sensitive screening.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Pleiotropic Effects of miR5504 Underlying Plant Height, Grain Yield and Quality in Rice.

MicroRNAs (miRNAs) are known to play critical roles in regulating rice agronomic traits through mRNA cleavage or translational repression. Our previous study indicated that miR5504 regulates plant height by affecting cell proliferation and expansion. Here, the two independent homozygous mir5504 mutants (CR1 and CR2) and overexpression lines (OE1 and OE2) were further used to investigate the functions of miR5504. The panicle length, 1000-grain weight and grain yield per plant of miR5504-OE lines were identical to those of Nipponbare (NIP), but the 1000-grain weight of mir5504 mutants was reduced by about 10% and 9%, respectively. Meanwhile, the grain width and thickness of mir5504 mutants decreased significantly by approximately 10% and 11%, respectively. Moreover, the cytological results revealed a significant decrease in cell number along grain width direction and cell width in spikelet in mir5504, compared with those in NIP. In addition, several major storage substances of the rice seeds were measured. Compared to NIP, the amylose content of the mir5504 seeds was noticeably decreased, leading to an increase of nearly 10 mm in gel consistency (GC) in mir5504 lines. Further investigation confirmed that LOC_Os08g16914 was the genuine target of miR5504: LOC_Os08g16914 over-expression plants phenocopied the mir5504 mutants. This study provides insights into the role of miR5504 in rice seed development.

Natural variation in BnaA9.NF-YA7 contributes to drought tolerance in Brassica napus L.

Rapeseed (Brassica napus) is one of the important oil crops worldwide. Its production is often threatened by drought stress. Here, we identify a transcription factor (BnaA9.NF-YA7) that negatively regulates drought tolerance through genome-wide association study in B. napus. The presence of two SNPs within a CCAAT cis element leads to downregulation of BnaA9.NF-YA7 expression. In addition, the M63I (G-to-C) substitution in the transactivation domain can activate low level expression of BnaA4.DOR, which is an inhibitory factor of ABA-induced stomatal closure. Furthermore, we determine that Bna.ABF3/4s directly regulate the expression of BnaA9.NF-YA7, and BnaA9.NF-YA7 indirectly suppresses the expression of Bna.ABF3/4s by regulation of Bna.ASHH4s. Our findings uncover that BnaA9.NF-YA7 serves as a supplementary role for ABA signal balance under drought stress conditions, and provide a potential molecular target to breed drought-tolerant B. napus cultivars.

Enrichment of Phosphopeptides Based on Zirconium Phthalocyanine-Modified Magnetic Nanoparticles.

Highly selective extraction of phosphopeptides is necessary before mass spectrometry (MS) analysis. Herein, zirconium phthalocyanine-modified magnetic nanoparticles were prepared through a simple method. The Fe-O groups on Fe3O4 and the zirconium ions on phthalocyanine had a strong affinity for phosphopeptides based on immobilized metal ion affinity chromatography (IMAC). The enrichment platform exhibited low detection limit (0.01 fmol), high selectivity (α-/ß-casein/bovine serum albumin, 1/1/5000), good reusability (10 circles), and recovery (91.1 ± 1.1%) toward phosphopeptides. Nonfat milk, human serum, saliva, and A549 cell lysate were employed as actual samples to assess the applicability of the enrichment protocol. Metallo-phthalocyanine will be a competitive compound for designing highly efficient adsorbents and offers a new approach to phosphopeptide analysis.

Cathepsin B: The dawn of tumor therapy.

Cathepsin B (CTSB) is a key lysosomal protease that plays a crucial role in the development of cancer. This article elucidates the relationship between CTSB and cancer from the perspectives of its structure, function, and role in tumor growth, migration, invasion, metastasis, angiogenesis and autophagy. Further, we summarized the research progress of cancer treatment related drugs targeting CTSB, as well as the potential and advantages of Traditional Chinese medicine in treating tumors by regulating the expression of CTSB.

Insights into drought stress response mechanism of tobacco during seed germination by integrated analysis of transcriptome and metabolome.

Drought stress inhibits seed germination, plant growth and development of tobacco, and seriously affects the yield and quality of tobacco leaves. However, the molecular mechanism underlying tobacco drought stress response remains largely unknown. In this study, integrated analysis of transcriptome and metabolome was performed on the germinated seeds of a cultivated variety K326 and its EMS mutagenic mutant M28 with great drought tolerance. The result showed that drought stress inhibited seed germination of the both varieties, while the germination rate of M28 was faster than that of K326 under drought stress. Besides, the levels of phytohormone ABA, GA19, and zeatin were increased by drought stress in M28. Five vital pathways were identified through integrated transcriptomic and metabolomic analysis, including zeatin biosynthesis, aspartate and glutamate synthesis, phenylamine metabolism, glutathione metabolism, and phenylpropanoid synthesis. Furthermore, 20 key metabolites in the above pathways were selected for further analysis of gene modular-trait relationship, and then four highly correlated modules were found. Then analysis of gene expression network was carried out of Top30 hub gene of these four modules, and 9 key candidate genes were identified, including HSP70s, XTH16s, APX, PHI-1, 14-3-3, SCP, PPO. In conclusion, our study uncovered some key drought-responsive pathways and genes of tobacco during seeds germination, providing new insights into the regulatory mechanisms of tobacco drought stress response.