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Abstract LiNi x Co y Mn1-x-y O2 (0 < x, y

Nanocellulose (CNF) was obtained from carrots using a combination of chemical treatment, mechanical milling, and ultrasonic treatment. Ultrafast preparation of maleic anhydride esterified nanocellulose was achieved by a hydrated hydrogen ion-driven dissociation, chemical cross-linking strategy based on a “one-pot” reaction method. Esterification modification with maleic anhydride reduced the crystallinity of nanocellulose and enhanced its thermal stability. High-strength drug-carrying hydrogels (MACNF/SA) with different drug loading capacities were prepared using cefixime (CFX) as a drug model and maleic anhydride esterified nanocellulose (MACNF) and sodium alginate (SA) as the main raw materials. The compressive strength of MACNF/SA hydrogels made from MACNF reached a maximum of 80.3 kPa when the mass ratio of CNF to MA was 2.5:12. Rheological property tests showed that the MACNF/SA hydrogels were pseudoplastic fluids with shear thinning. The drug release from the drug-carrying hydrogels followed non-Fickian diffusion.

Heat moisture treatment (HMT) is a widely used method for starch modification due to its safe, green, environmentally friendly, and efficient characteristics. In this study, quinoa starch was modified by HMT with varying moisture contents (20%, 25%, and 30%) at a temperature of 120 °C for 10 hours, and its rheological properties, in vitro digestive properties, and structure were further evaluated. The steady rheology results indicated that all the samples were shear-thinning non-Newtonian fluids. After undergoing HMT, the apparent viscosity of quinoa starch decreased significantly. Additionally, the samples' apparent viscosity decreased as the moisture content of HMT increased. The results of frequency sweep showed that the storage modulus G' of quinoa starch decreased with the increase of moisture content of HMT, while the loss modulus G'', and loss factor increased with the increase of moisture content of HMT. The thixotropic results showed that the thixotropic ring area of quinoa starch decreased with the increase of the moisture content of HMT. Additionally, the results of in vitro digestion showed that the content of rapidly digestible starch in quinoa starch decreased significantly after HMT (P

Abstract Intervertebral disc degeneration is a natural process during aging and a leading cause of lower back pain. Here, we generate a comprehensive atlas of nucleus pulposus cells using single-cell RNA-seq analysis of human nucleus pulposus tissues (three males and four females, age 41.14 ± 18.01 years). We identify fibrotic late-stage nucleus pulposus cells characterized by upregulation of serglycin expression which facilitate the local inflammatory response by promoting the infiltration of inflammatory cytokines and macrophages. Finally, we discover that daphnetin, a potential serglycin ligand, substantially mitigates the local inflammatory response by downregulating serglycin expression in an in vivo mouse model, thus alleviating intervertebral disc degeneration. Taken together, we identify late-stage nucleus pulposus cells and confirm the potential mechanism by which serglycin regulates intervertebral disc degeneration. Our findings indicate that serglycin is a latent biomarker of intervertebral disc degeneration and may contribute to development of diagnostic and therapeutic strategies.

Atractylodis Macrocephalae Rhizoma (AMR) is a traditional Chinese medicine used for gastrointestinal diseases. With increased demand, there are more and more places of cultivation for AMR. However, the quality of AMR varies from place to place, and there is no good way to distinguish AMR from different origins at present. In this paper, we determined the content of eight chemical components including 60% ethanol extracts, essential oil, polysaccharides, atractylenolides, and atractylone, obtained the color parameters of AMR powder by colorimetry, and odor information was captured by the electronic nose, all of which were combined with machine learning to establish a rapid origin traceability method. The results of the principal component analysis of the chemical components revealed that Zhejiang AMR has a high comprehensive score and overall better quality. The Kruskal–Wallis test demonstrated that there are varying degrees of differences in chemical composition and color parameters across the different origin. However, the accuracy of the classification model is low (less than 80%), making it difficult to distinguish between different origins of AMR. The electronic nose demonstrated excellent classification performance in the traceability of AMR from different origins, with accuracy reaching more than 90% (PLS-DA: 96.88%, BPNN: 96.88%, PSO-SVM: 100%). Overall, this study clarified the quality differences of AMR among different origins, and a rapid and precise method combining machine learning was developed to trace the origin of AMR.

Of late, intensive agricultural practices are often associated with many negative implications for soil systems, such as decline in soil organic matter and biological diversity, and increase in the risk of soil erosion, and degradation of soil physical quality. However, conservation agriculture (CA) offers minimum soil disturbance, improving and creating more efficient use of natural resources, enhancing use efficiency of external inputs and increasing soil functioning, organic matter content, and biodiversity. However, CA practices need to be redefined at the specific field level or growing conditions to promote sustainable production system without deteriorating soil health, and to manage profitability. Therefore, a fixed-site field experiment was conducted to evaluate the effects of different tillage and residue management practices; CT0 (puddled transplanted rice followed by conventional tilled wheat sown with residue removal); CTR (puddled transplanted rice followed by conventional tilled wheat sown with residue retention); NT0 (direct seeded rice followed by zero-tilled wheat sown with residue removal); NTR (direct seeded rice followed by zero-tilled wheat sown with residue retention) on soil organic carbon (SOC) stock and grain yield of rice–wheat double cropping system. The results showed that the mean SOC stock increased by 31%, 21.9% and 15.3% and by 35.2%, 22% and 17% under NTR, CTR, and NT0 compared with CT0. Furthermore, the SOC sequestration rate in NTR was significantly higher than other treatments. Particulate organic carbon (POC) concentration was 58.4% higher in 0–15 cm than 15–30 cm soil layer, and the maximum concentration was recorded under NTR and minimum was under CT0. The highest average rice grain yield was recorded under CTR, which was 5.5%, 16.2% and 24.3% higher than CT0, NTR and NT0, respectively. Both NTR and CTR produced more average wheat yield, which was 8.4% and 8.5% higher than CT0 and NT0, respectively. Principal component analysis (PCA) also showed that cumulative cropping system yield and SOC were increased under CTR, NTR as compared to other treatments. Therefore, it can be drawn that residue retention could be a useful management practice to increase SOC stock and grain yield under both tillage practices in the rice–wheat cropping system.

Biodegradable transparent wood was fabricated by introducing epoxy resin E51 modified with the silane coupling agent (KH550) into bleached poplar veneer. The light transmittance of transparent wood was modulated by KH550 content. The silane coupling agent KH550 was able to change the compatibility of epoxy resin and wood substrate, thereby affecting the performance of transparent wood. In this study, the effect of KH550 on the properties of transparent wood and its mechanism were investigated. The light transmittance, tensile strength, and elongation at break of transparent wood showed an increasing and then decreasing trend with increased KH550 dosage. When the mass ratio of the coupling agent KH550 to the epoxy resin was 1:20, the transparent wood made by impregnating wood substrate with epoxy resin modified by KH550 had the best performance, with the fast degradation starting temperature of 338 °C, 81.07% light transmittance at λ = 780 nm, 59.92 MPa tensile strength, and 3.47% elongation at break. This work provides a new way for preparing high performance transparent wood.

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

Background: Drosophila melanogaster is a well-studied and highly tractable genetic model system for deciphering the molecular mechanisms underlying various biological processes. Although being one of the most critical post-translational modifications of proteins, the understanding of glycosylation in Drosophila is still lagging behind compared with that of other model organisms. Methods: In this study, we systematically investigated the site-specific N-glycan profile of Drosophila melanogaster using intact glycopeptide analysis technique. This approach identified the glycans, proteins, and their glycosites in Drosophila, as well as information on site-specific glycosylation, which allowed us to know which glycans are attached to which glycosylation sites. Results: The results showed that the majority of N-glycans in Drosophila were high-mannose type (69.3%), consistent with reports in other insects. Meanwhile, fucosylated N-glycans were also highly abundant (22.7%), and the majority of them were mono-fucosylated. In addition, 24 different sialylated glycans attached with 16 glycoproteins were identified, and these proteins were mainly associated with developmental processes. Gene ontology analysis showed that N-glycosylated proteins in Drosophila were involved in multiple biological processes, such as axon guidance, N-linked glycosylation, cell migration, cell spreading, and tissue development. Interestingly, we found that seven glycosyltransferases and four glycosidases were N-glycosylated, which suggested that N-glycans may play a regulatory role in the synthesis and degradation of N-glycans and glycoproteins. Conclusions: To our knowledge, this work represents the first comprehensive analysis of site-specific N-glycosylation in Drosophila, thereby providing new perspectives for the understanding of biological functions of glycosylation in insects.

Summary: DNA G-quadruplex is a non-canonical secondary structure that could epigenetically regulate gene expression. To investigate the regulating role of G-quadruplex, we devised an integrating method to perform the algorithm profiling and genome-wide analysis for the dynamic change of genomic G-quadruplex and RNA profiles in rat nucleus pulposus cells by inducing G-quadruplex folding with multiple stabilizers. A group of genes potentially regulated by G-quadruplex and involved in the inflammation process has been identified. We found that G-quadruplex folding triggers inflammation response by upregulating inflammatory cytokines, which could promote G-quadruplex folding in a manner of positive feedback loop. Moreover, we confirmed that G-quadruplex is a marker indicating elevated inflammatory status and G-quadruplex folding facilitates the development of inflammatory diseases using in vivo intervertebral disc degeneration models. The crosstalk between G-quadruplex and inflammatory cytokines plays a vital role in regulating inflammation-derived diseases, which may provide new insights into the blocking target.

Under the background of the severe human health and world economic burden caused by COVID-19, the attenuation of vaccine protection efficacy, and the prevalence and immune escape of emerging variants of concern (VOCs), the third dose of booster immunization has been put on the agenda. Systems biology approaches can help us gain new perspectives on the characterization of immune responses and the identification of factors underlying vaccine-induced immune efficacy. We analyzed the antibody signature and transcriptional responses of participants vaccinated with COVID-19 inactivated vaccine and protein subunit vaccine as a third booster dose. The results from the antibody indicated that the third booster dose was effective, and that heterologous vaccination with the protein subunit vaccine as a booster dose induced stronger humoral immune responses than the homologous vaccination with inactivated vaccine, and might be more effective against VOCs. In transcriptomic analysis, protein subunit vaccine induced more differentially expressed genes that were significantly associated with many important innate immune pathways. Both the homologous and heterologous boosters could increase the effectiveness against COVID-19, and compared with the inactivated vaccine, the protein subunit vaccine, mediated a stronger humoral immune response and had a more significant correlation with the innate immune function module, which provided certain data support for the third booster immunization strategy.

Abstract As the core ingredient for spin polarization, the equilibrium spin distribution function that eliminates the collision terms is derived from the detailed balance principle. The kinetic theory for interacting fermionic systems is applied to the Nambu–Jona-Lasinio model at quark level. Under the semi-classical expansion with respect to $$\hbar $$ ħ , the kinetic equations for the vector and axial-vector distribution functions are obtained with collision terms. For an initially unpolarized system, spin polarization can be generated at the first order of $$\hbar $$ ħ from the coupling between the vector and axial-vector charges. Different from the classical transport theory, the collision terms in a quantum theory vanish only in global equilibrium with Killing condition.

Abstract We provide an analytical tripartite-study from the generalized R-matrix. It provides the upper bound of the maximum violation of Mermin’s inequality. For a generic 2-qubit pure state, the concurrence or R-matrix characterizes the maximum violation of Bell’s inequality. Therefore, people expect that the maximum violation should be proper to quantify Quantum Entanglement. The R-matrix gives the maximum violation of Bell’s inequality. For a general 3-qubit state, we have five invariant entanglement quantities up to local unitary transformations. We show that the five invariant quantities describe the correlation in the generalized R-matrix. The violation of Mermin’s inequality is not a proper diagnosis due to the non-monotonic behavior. We then classify 3-qubit quantum states. Each classification quantifies Quantum Entanglement by the total concurrence. In the end, we relate the experiment correlators to Quantum Entanglement.

Rotating machinery is susceptible to harsh environmental interference, and fault signal features are challenging to extract, leading to difficulties in health status recognition. This paper proposes multi-scale hybrid features and improved convolutional neural networks (MSCCNN) health status identification methods for rotating machinery. Firstly, the rotating machinery vibration signal is decomposed into intrinsic modal components (IMF) using empirical wavelet decomposition, and multi-scale hybrid feature sets are constructed by simultaneously extracting time-domain, frequency-domain and time-frequency-domain features based on the original vibration signal and the intrinsic modal components it decomposes. Secondly, using correlation coefficients to select features sensitive to degradation, construct rotating machinery health indicators based on kernel principal component analysis and complete health state classification. Finally, a convolutional neural network model (MSCCNN) incorporating multi-scale convolution and hybrid attention mechanism modules is developed for health state identification of rotating machinery, and an improved custom loss function is applied to improve the superiority and generalization ability of the model. The bearing degradation data set of Xi’an Jiaotong University is used to verify the effectiveness of the model. The recognition accuracy of the model is 98.22%, which is 5.83%, 3.30%, 2.29%, 1.52%, and 4.31% higher than that of SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features, respectively. The PHM2012 challenge dataset is used to increase the number of samples to validate the model effectiveness, and the model recognition accuracy is 97.67%, which is 5.63%, 1.88%, 1.36%, 1.49%, and 3.69% higher compared to SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features methods, respectively. The MSCCNN model recognition accuracy is 98.67% when validated on the degraded dataset of the reducer platform.

The urgent approval of the use of the inactivated COVID-19 vaccine is essential to reduce the threat and burden of the epidemic on global public health, however, our current understanding of the host immune response to inactivated vaccine remains limited. Herein, we performed serum IgG antibody detection and transcriptomics analysis on 20 SARS-CoV-2 naïve individuals who received multiple doses of inactivated vaccine and 5 SARS-CoV-2 recovered individuals who received single dose of inactivated vaccine. Our research revealed the important role of many innate immune pathways after vaccination, identified a significant correlation with the third dose of booster vaccine and proteasome-related genes, and found that SARS-CoV-2 recovered individuals can produces a strong immune response to a single dose of inactivated vaccine. These results help us understand the reaction mechanism of the host’s molecular immune system to the inactivated vaccine, and provide a basis for the choice of vaccination strategy.

Multi-break vacuum circuit breakers (VCBs) is an important research direction for scaling VCBs up to a higher voltage level. A VCB module with an integrated operation of double-break is designed to increase synchronization of multi-break VCBs, and the synchronization error beyond the mechanical structure is eliminated. First, the mathematical model and motion model of the transmission system were established. Then the motion characteristics and the double-break synchronization error of the module were analyzed. Finally, the prototype was designed and manufactured for parameter testing and trajectory verification. The analysis shows that the displacement transmission ratio increases with the increase in the transmission pull rod length and the initial closing angle. The length of the transmission pull rod has little effect on the displacement transmission ratio, while the initial closing angle has a major effect on it. The simulation results show that the transmission system can realize maximum control of the double-break mechanical error to within 0.59 mm with the IT12 level and the mechanical error obtained by the root sum squared method can be within 0.3 mm. The maximum mechanical error can be controlled to within 0.06 mm by improving the tolerance and fit of moving pairs to the IT6 level. The experimental results reveal that the maximum mechanical error of double-break’s clearance between open contacts is 0.49 mm and the synchronization error is not greater than ±0.1 ms. It is proved that the module can realize high precision synchronous operation. This study provides a practical design idea and processing requirements for the control of synchronous errors of multi-break VCBs.

We study Casimir effect in equilibrium and non-equilibrium photon gas in the frame of quantum kinetic theory for U (1) gauge field. We derive first the transport, constraint and gauge fixing equations for the photon number distribution from Maxwell’s equations, and then calculate the energy variation and Casimir force for a finite system by considering boundary condition on the surface of the system. The Casimir force in vacuum is suppressed by the thermal motion of photons in equilibrium state, when considering two adiabatic plates. In non-equilibrium state, the photon induced Casimir force oscillates and decays with time and finally disappears.

It is important to understand the strong external magnetic field generated at the very beginning of heavy ion collisions. We study the effect of the magnetic field on the anisotropic charmonium formation in Pb+Pb collisions at the LHC energy. The time dependent Schrödinger equation is employed to describe the motion of cc¯ pairs. We compare our model prediction of the non-collective anisotropic parameter v2 of J/ψ with CMS data at high transverse momentum.

The development of high-performance solid-state lithium-ion batteries (LIBs) requires designing solid-state electrolytes (SEs) with high ionic conductivity and excellent electrochemical stability. Here, we report 130 novel materials as promising SEs, identified through large-scale high-throughput calculations. These calculations employed a universal machine-learning interatomic potential (ML-IAP) as a surrogate model for density functional theory (DFT). In calculating the Li+ conductivity of well-known conductors, we found that the universal ML-IAP tends to underestimate the activation energy for Li+ conductivity by about 150 meV compared to DFT calculations. To identify practical SEs, we parameterized screening criteria for key material properties including synthesizability, interfacial stability, and ionic conductivity. We then established a tiered workflow for the accurate and efficient assessment of potential SEs. Additionally, we developed a tree-based ML model to unravel the relationship between structure, chemistry, and conductivity. Our findings indicate that features such as maximum packing efficiency, volume per atom, packing fraction, and differences in electronegativity are crucial in influencing Li+ conduction. This work not only introduces novel SEs vital for the advancement of solid-state LIBs but also showcases the potential of ML in material innovation. [ABSTRACT FROM AUTHOR]