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Ecological stoichiometry is essential for investigating biogeochemical cycling in an ecosystem. Thinning, a management practice that closely mimics natural processes, significantly influences stand structure and microclimate, thereby affecting nutrient cycling. Nonetheless, seasonal variations in ecological stoichiometry across the leaf-litter-soil continuum under different thinning regimes remain inadequately understood. In this study, we evaluated three thinning methods (strip filling (SF), ecological thinning (ET), and forest gap (FG)) to investigate the stoichiometric characteristics of Cupressus funebris Endl (C. funebris). within the leaf-litter-soil system in Southwest China. The samples were collected during four distinct seasonal periods: early dry season (January–March, EDS), late dry season (April–June, LDS), early wet season (July–September, EWS), and late wet season (October–December, LWS). The results indicated that the (1) carbon (C), nitrogen (N), and phosphorus (P) contents and C:N:P ratio in leaves, litter, and soils varied widely and were strongly influenced by thinning method and season. (2) In the EDS, the soil TP content significantly decreased by 36.9% (p < 0.05), 41.67% (p < 0.05), and 17.9% (p < 0.05) under ET, FG, and SF treatments compared to the pure C. funebris forest (PC). (3) Compared to the PC, the leaf organic C content under ET significantly increased by 6.6% (EDS, p < 0.05), 8.4% (EWS, p < 0.05), 24.8% (LDS, p < 0.05), and 11.5% (EWS, p < 0.05). (4) Under identical thinning methods, the contents of litter C, litter N, litter P, leaf N, and leaf P (excluding litter C in SF) were found to be highest in the LWS. Conversely, the ratios of litter C:N, litter C:P, litter N:P, leaf C:N, leaf C:P, leaf N:P, soil N:P, and soil C:P (except for the ratios of litter N:P in ET and FG) were observed to be lowest in the LWS. (5) Season and thinning method significantly affected the internal stability of P stoichiometric homeostasis, and litter P under ET (EWS) was categorized as "plastic" (p < 0.1, 0.75 < H). (6) The results of the structural equation model show that the thinning method has a direct positive impact on leaf C, N, and P contents and a direct negative impact on the chemical stoichiometry of leaves and soil. Season has a direct positive impact on soil C, N, and P contents, as well as on the chemical stoichiometry of litter and leaves; however, they have a direct negative impact on leaf C, N, and P contents. This study contributes to C. funebris plantation management and provides basic information for global stoichiometric analysis. [ABSTRACT FROM AUTHOR]

Curcumin, derived from Curcuma longa (turmeric), exhibits significant potential in dermatology, addressing conditions like atopic dermatitis, psoriasis, chronic wounds, skin cancer, and infections through its anti-inflammatory, antioxidant, anticancer, and antimicrobial properties. This review synthesizes evidence on curcumin's mechanisms, including modulation of immune responses and promotion of wound healing, showcasing its efficacy in reducing inflammation, cytokine levels, and enhancing skin barrier functions. Studies highlight curcumin's ability to selectively target tumor cells, suggesting a multifaceted approach to cancer therapy with minimal side effects. Despite promising therapeutic benefits, challenges remain in bioavailability, potency, and targeted delivery, underscoring the need for further research to optimize dosages, delivery methods, and assess long-term safety. The integration of curcumin into dermatological practice requires a balanced consideration of evidence-based efficacy and safety. Curcumin's comprehensive utility in dermatology, coupled with the necessity for advanced scientific exploration, emphasizes the importance of combining traditional knowledge with contemporary research to improve patient care in dermatology. This approach could significantly enhance outcomes for individuals with skin-related conditions, marking curcumin as a versatile and promising agent in the field. [ABSTRACT FROM AUTHOR]

Anomalies in water quality, which frequently arise due to pollution, constitute a substantial menace to human health. The preservation of public welfare critically entails the timely recognition of abnormal water quality. Conventional techniques for detecting water quality anomalies face obstacles such as the necessity of expert knowledge, limited accuracy in detection, and delays in identification. In this paper, we proposed an original unsupervised technique for identifying water quality anomalies combined with time-frequency analysis and clustering (TCAD). We chose time-frequency analysis because it effectively evaluates water quality changes, generating distinct multi-band signals that reflect different aspects of water quality dynamics. We also proposed a clustering technique which can identify water quality markers and amalgamate data from multi-band signals for accurate anomaly detection. We seek to clarify the reasoning behind our methodology by portraying how time-frequency analysis and clustering address the deficiencies of conventional methods. Our experiments evaluated various indicators of water quality, and the effectiveness of our proposed approach was supported by comparative analyses with commonly used models for detecting anomalies in water quality. [ABSTRACT FROM AUTHOR]

BACKGROUND: Tumbling treatment is widely used in the production of cooked ham. However, traditional intermittent tumbling (IT) treatment is time‐consuming. To enhance the tumbling efficiency, high‐intensity ultrasound was used to assist IT treatment (UIT). RESULTS: UIT treatment reduced the tumbling time and significantly improved the water holding capacity, tenderness, sliceability and texture of cooked ham compared to IT treatment. Furthermore, more violent destruction of meat tissue was exhibited in the UIT treatment. This change facilitated extraction of more salt‐soluble protein, which in turn welded meat pieces tightly and improved the quality of the cooked ham. CONCLUSION: UIT treatment could accelerate the tumbling process and enhance the quality of cooked ham. These results may provide guidance on effective strategies for a high‐quality meat production process. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Morphing aircraft are able to keep optimal performance in diverse flight conditions. However, the change in geometry always leads to challenges in the design of flight controllers. In this paper, a new method for designing a flight controller for variable-sweep morphing aircraft is presented—dynamic inversion combined with L 1 adaptive control. Firstly, the dynamics of the vehicle is analyzed and a six degrees of freedom (6DOF) nonlinear dynamics model based on multibody dynamics theory is established. Secondly, nonlinear dynamic inversion (NDI) and incremental nonlinear dynamic inversion (INDI) are then employed to realize decoupling control. Thirdly, linear quadratic regulator (LQR) technique and L 1 adaptive control are adopted to design the adaptive controller in order to improve robustness to uncertainties and ensure the control accuracy. Finally, extensive simulation experiments are performed, wherein the demonstrated results indicate that the proposed method overcomes the drawbacks of conventional methods and realizes an improvement in control performance. [ABSTRACT FROM AUTHOR]

In order to better study the chosen path of the consumption model of public green energy and more accurately predict consumers' green energy consumer behavior, we take new energy vehicles as an example to explore the driving mechanism and internal mechanism of the public green energy consumption model from the perspective of motivation. We propose an ensemble learning model based on a stacking strategy. The model uses XGBoost, random forest and gradient lifting decision trees as primary learners to transform features, and uses logistic regression as a meta-learner to predict users' consumer behavior. The experimental results show that this feature engineering method can significantly improve the accuracy rate in multiple model algorithms, and the prediction effect of the ensemble learning model is better than that of a single model, with the accuracy rate of 82.81%. In conclusion, the ensemble learning model based on a stacking strategy can effectively predict the public's consumer behavior. This provides a theoretical basis and policy recommendations for promoting green energy products represented by new energy vehicles, thereby improving the practical path for proposing green energy consumption. [ABSTRACT FROM AUTHOR]

Using split Hopkinson pressure bar (SHPB) to carry out the Brazilian disk test, for the calculation of the splitting tensile strain rate of the specimen, the method in the existing literature only considers the influence of the tensile stress, while in the actual splitting tensile process, the combined effect of compressive stress and tensile stress leads to a large error between the calculated strain rate and the measured strain rate. Therefore, the calculation of splitting tensile strain rate by the existing method is not accurate. To solve this problem, a new analytical method considering the tension–compression coupling was proposed in this study. The true strain rate of specimens can be obtained using the classical three-wave method of SHPB test and the basic performance parameters of bars and specimens. The results of this method were verified by numerical calculation and experiment. The results show that the strain–time history curve calculated by this method is highly consistent with the numerical results in terms of numerical verification, and the error range of strain rate is 1.2–8.6%. However, the error range of strain rate obtained using the existing method is 34.4–41.2%. The rising stage of strain–time history curve calculated by this method is highly consistent with the experimental results in terms of experimental verification, and the error range of strain rate is 1.7–14.7%. While the error range of strain rate obtained using the existing method is 47.2–58.5%. The existing method of splitting tensile strain rate will overestimate the dynamic tensile strength of material and make engineering design in a dangerous state. [ABSTRACT FROM AUTHOR]

To advance the industrialization production of steamed buns, the current study explored the freeze-stability of unfermented, pre-fermented and par-steamed frozen dough. The results showed that the steamed bun made from unfermented dough with 2.0% yeast, the pre-fermented dough with a pre-fermented time of 30 min and the par-steamed dough with a pre-steamed time of 15 min showed the best sensory properties quality upon frozen storage. The gassing power of un- and pre-fermented dough gradually decreased, and dough with longer pre-fermented time exhibited more evident loss of gassing power. Freeze-induced depolymerization of gluten protein was the least distinct in the par-steamed dough, followed by the pre- and un-fermented dough, which was probably related to the superior freeze stability of glutenin-gliadin macro-crosslinks upon the pre-steaming stage. The surface hydrophobicity of gluten proteins of frozen dough decreased during the initial storage and was enhanced subsequently, which was related with the combined effects of the unfolding and synchronous aggregation induced by freezing and steaming, respectively. Moreover, the surface hydrophobicity of gluten in par-steamed frozen dough and steamed buns was more resistant to frozen storage, which was probably attributed to the established stable structure during the pre-steaming process. [ABSTRACT FROM AUTHOR]

Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody–antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling. [ABSTRACT FROM AUTHOR]

Conversion of the greenhouse gas CO2 to value‐added products is an important challenge for sustainable energy research. Here, a durably nanohybrid composed of Ag nanoparticles and polyacrylamide was constructed for the selectively electroreduction of CO2 to CO. The nanohybrid exhibited an outstanding CO faradaic efficiency of 97.2±0.2 % at −0.89 VRHE (vs. the reversible hydrogen electrode) with a desirable CO partial current density of −22.0±2.3 mA cm−2 and maintained the CO faradaic efficiency above 95 % over a wide potential range (−0.79 to −1.09 VRHE), showing excellent stability during a 48 h prolonged electrolysis. The origins of selective enhancement of CO2 reduction over the nanohybrid stemmed from the activation of CO2 via hydrogen bond and the low basicity of the amide. DFT calculations implied that the synergy of Ag nanoparticles and amide could better stabilize the key intermediate (*COOH) and effectively lower the overpotential of CO2 reduction. These results establish the synergistic effects of organic/inorganic hybrid as a complementary method for tuning selectivity in CO2‐to‐fuels catalysis. [ABSTRACT FROM AUTHOR]

Nanoparticles (NPs) have demonstrated a potential for hepatocarcinoma therapy. However, the effective and safe NP-mediated drug transportation is still challenging due to premature leakage and inaccurate release of the drug. Herein, we designed a series of core cross-linking galactose-based glycopolymer-drug conjugates (GPDs) NPs with both redox-responsive and pH-sensitive characteristics to target and program drug release. Glycopolymer is comprised of galactose-containing units, which gather on the surface of GPD NPs and exhibit specific recognition to hepatocarcinoma cells, which over-express the asialoglycoprotein receptor. GPD NPs are stable in a normal physiological environment and can rapidly release the drug in hepatocarcinoma cells, which are reductive and acidic, by combining disulfide bond cross-linked core, as well as boronate ester-linked hydrophilic glycopolymer chain and the hydrophobic drug. [ABSTRACT FROM AUTHOR]

Abstract: The high metastatic potential of non-small cell lung cancers (NSCLCs) is closely correlated with the elevated expression of vascular endothelial growth factor (VEGF) and resultant tumor angiogenesis. However, no effective strategies against VEGF expression have been available in NSCLCs therapy. This study demonstrated that elevated reactive oxygen species (ROS) levels derived from both mitochondria and NADPH oxidase were required for VEGF expression in NSCLC cells. Atorvastatin administration could significantly inhibit VEGF expression both in vitro and in vivo via inhibition of ROS production. Atorvastatin inhibited ROS generation partly through suppression of Rac1/NADPH oxidase activity. Specifically, atorvastatin could upregulate the activity of glutathione peroxidase (GPx) and catalase, which are responsible for elimination of hydrogen peroxide (H2O2) in the mitochondria and peroxisomes, respectively. Thus, inhibition of ROS production by concomitant suppression of Rac1/NADPH oxidase activity and upregulation of the activity of GPx and catalase contributes critically to atorvastatin-reduced VEGF expression in NSCLCs. Atorvastatin may be a potential alternative against VEGF expression and angiogenesis in NSCLCs therapy. [Copyright &y& Elsevier]

Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-β-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future. [ABSTRACT FROM AUTHOR]

To investigate fatigue tensile behaviour of air-entrained concrete after the freeze–thaw damage, fatigue tensile tests with four different loading paths were conducted on air-entrained concrete after 0, 100, 200 and 400 freeze–thaw cycles. The four different loading paths contained the monotonic (M) test where the envelope stress–strain curve was obtained, the cycles with constant strain increment (CSI) test where the variation of elastic modulus on the whole stress–strain curve was studied, the cycles to variable maximum strain amplitude (VMS) test where the low-cycle fatigue behaviour at different strain levels was analysed and the cycles with CMS' test, which was designed to analyse the post-peak behaviour of the specimens. Experimental results indicated that the properties of the air-entrained concrete basically remained unchanged under 200 freeze–thaw cycles, including the mass loss rate, tensile strength, elastic modulus and the dissipated energy per unit volume. While the freeze–thaw cycles increased over the critical value, the energy resulted from the cyclic load was not released from the materials and accumulated inside the materials fast. Energy accumulation directly led to the deterioration of the air-entrained concrete. To observe the pore structure of the air-entrained concrete, the scanning electron microscope test (SEM) was also adopted in this paper. [ABSTRACT FROM AUTHOR]