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Immobilized amines adsorbents have excellent CO2 adsorption capacity and low regeneration energy consumption, which have broad application prospect, but poor reusability. To improve the cyclic adsorption performance of immobolized amines for CO2 capture, the mesoporous silica A‐MSU‐J with carboxyl groups on the surface was synthesized, and immobilized amine A‐MSU‐J‐TEPA was prepared by introducing tetraethylenepentamine (TEPA) by impregnation method. The introduction of carboxyl groups onto the carrier surface can form stronger hydrogen bonds with TEPA than the initial hydroxyl groups, thus achieving stable loading of TEPA. When the TEPA load was 50 wt %, the adsorption capacity of 4A‐65MSU‐J‐50TEPA declined by only 13.7 % (from 2.58 to 2.23 mmol ⋅ g−1, at 70 °C, 0.1 Mpa) after 10 cycles, while the adsorption capacity of 65MSU‐J‐50TEPA decreased by 23.6 % (from 2 to 1.53 mmol ⋅ g−1). Furthermore, carrier pore size also affects the adsorption capacity of immobilized amines for CO2. The adsorption capacity of 4A‐65MSU‐J‐50TEPA with larger pore size (2.95 mmol ⋅ g−1, at 55 °C) is much higher than that of 4A‐25MSU‐J‐50TEPA with smaller pore size (2.17 mmol ⋅ g−1). In conclusion, improving the interaction between organic amines and the carrier surface and using carriers with larger pore size can enhance the adsorption capacity and reusability of immobolized amines adsorbents. [ABSTRACT FROM AUTHOR]

Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel2) are easily overlooked. To address the problems of complex background interference, difficult detection of tiny targets and biased detection of prediction boxes that exist in standard detectors, in this paper, we constructed a tiny target dataset TTALDD-4 containing four types of diseases, which include Alternaria leaf spot, Frogeye leaf spot, Grey spot and Rust, and proposed the HSSNet detector based on the YOLOv7-tiny benchmark for professional detection of apple leaf disease tiny targets. Firstly, the H-SimAM attention mechanism is proposed to focus on the foreground lesions in the complex background of the image. Secondly, SP-BiFormer Block is proposed to enhance the ability of the model to perceive tiny targets of leaf diseases. Finally, we use the SIOU loss to improve the case of prediction box bias. The experimental results show that HSSNet achieves 85.04% mAP (mean average precision), 67.53% AR (average recall), and 83 FPS (frames per second). Compared with other standard detectors, HSSNet maintains high real-time detection speed with higher detection accuracy. This provides a reference for the automated control of apple leaf diseases. [ABSTRACT FROM AUTHOR]

In this article, the problem of decentralized full-state constrained adaptive tracking control is investigated for interconnected time delay systems with input saturation. The extreme learning machine is applied to approximate the unknown nonlinear function in the design process. By selecting the appropriate Lyapunov–Krasovskii function, the time delay interconnection term could be compensated. A unified barrier function is introduced to ensure that the constraints imposed on all states are not violated. The Nussbaum function is introduced to deal with the problem of input saturation. By applying adaptive backstepping procedures, a novel decentralized full-state constrained adaptive tracking control strategy is proposed for the considered interconnected time delay systems. In terms of Lyapunov stability theory, it is proved that all the signals of the closed-loop systems are semi-global ultimately uniformly bounded and all state variables could be constrained within the time varying asymmetric boundaries. Finally, the effectiveness of the proposed scheme is illustrated by a simulation example of a two-stage chemical reactor. [ABSTRACT FROM AUTHOR]

In this study, a prescribed performance adaptive fault tolerant tracking control scheme is presented for a class of nonlinear large‐scale systems with time delay interconnection, dead zone input, and actuator fault. The radial basis function neural networks are used to approximate unknown nonlinear functions. Different from the barrier Lyapunov functions used to achieve the symmetrical prescribed performance, a new error transformation is introduced in this study to achieve the desired asymmetrical prescribed performance. In addition, Nussbaum function is introduced to solve the difficulties caused by dead zone input and actuator fault. Based on the appropriate Lyapunov–Krasovskii functions, the effect of time delay interconnection could be compensated. By using backstepping procedures, an adaptive fault tolerant tracking control approach is developed for the considered large‐scale systems, and the stability of the closed‐loop systems is analyzed by Lyapunov theory. Meanwhile, the prescribed performance of the tracking error could be guaranteed. Finally, the effectiveness of the proposed control approach is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

Osteoarthritis is a common chronic bone and joint disease, which is characterized by degenerative changes and destruction of articular cartilage, secondary hyperostosis. This study aimed to investigate the clinical severity and mechanism of S100A12 in patients with osteoarthritis. Serum samples were obtained from patients with osteoarthritis or normal volunteer in Minhang Branch of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine affiliated to Shanghai University of Traditional Chinese Medicine (Shanghai, China). C57BL/6J mice performed Resection of the medial collateral ligament and medial meniscus as mice model. MC3T3-E1 cells were induced with 100 ng of LPS as vitro model. The serum level of S100A12 was increased in patients with osteoarthritis. Similarly, S100A12 levels of serum and bone tissue from mice model of osteoarthritis were also higher than those of sham group. Over-expression of S100A12 promoted inflammation levels while down-regulation of S100A12 decreased inflammation levels in in vitro model of osteoarthritis. NLRP3 is an important target of S100A12 in pro-inflammation effects of osteoarthritis. NLRP3 was involved in the effects of S100A12 on inflammation in in vitro model of osteoarthritis. S100A12 also accelerated inflammation by NLRP3 in mice model of osteoarthritis. We conclude that serum S100A12 levels was a possible clinical severity, open inflammation of osteoarthritis model by NLRP3 and its receptors may be effective in preventing the development of osteoarthritis. [ABSTRACT FROM AUTHOR]

More effective boron-containing compounds are needed for use in boron neutron capture therapy (BNCT). Here, borate esters were synthesized by heating and dehydrating nucleotides adenosine triphosphate (ATP), adenosine diphosphate (ADP), adenosine monophosphate (AMP), the nucleoside (inosine) or glycerol in the presence of boric acid (H3BO3). Borate ester products were compared to clinical boron agent boronophenylalanine (BPA) and several other borate esters for neutron-sensitization effects using the A549 cell line. Cells were incubated with boron agent solutions (2.3 mM) for 5 h, then washed, resuspended in fresh media, and irradiated with a neutron dose of 0.33 Sv followed by cell survival assessment using the CCK-8 method. Calculated radiosensitization values (control group cell survival rate/boron agent-treated experimental group cell survival rate) were 3.9 ± 0.2 (ATP borate ester), 2.4 ± 0.1 (BPA), 2.1 ± 0.1 (ADP borate ester), 1.9 ± 0.2 (AMP borate ester), 1.7 ± 0.3 (glycerin borate ester), 1.4 ± 0.1 (inosine borate ester), 1.3 ± 0.3 (triethanolamine borate ester) and 1.3 ± 0.5 (H3BO3). Borate esters derived from nucleotides ATP, ADP or AMP exhibited significantly higher sensitization values than did those derived from glycerol, inosine or triethanolamine. Notably, due to its relatively higher water solubility and degree of tumor cell enrichment, ATP borate ester exhibited the highest sensitization rate overall, significantly exceeding rates obtained for BPA and borate esters of ADP and AMP. Flow cytometric determinations of boron agent-treated cell survival at 24 h postirradiation revealed long-term apoptosis rates of 4.8–6.6 ± 0.2% (nucleotide borate ester groups) and 5.6 ± 0.3% (BPA group) compared to 3.9 ± 0.1% (irradiation control group without boron agent) and 2.6 ± 0.2% (blank control group). Significant differences between experimental and control groups demonstrated that nucleotide borate esters and BPA induced long-term radiosensitization effects. In particular, postirradiation percentages of ATP borate ester-treated cells progressing to DNA replication prophase (G1 phase) increased significantly, while percentages of cells progressing to S phase significantly decreased, demonstrating cellular DNA replication inhibition. Meanwhile, boron content values of tumor tissue, measured using inductively coupled plasma mass spectrometry (ICP-MS) and expressed as tumor-to-normal tissue boron ratios (T/N), were not significantly different between nucleotide borate ester- and BPA-fed groups of tumor-bearing mice. However, tumor tissue boron concentrations of nucleotide borate ester-fed mice (0.81–0.88 ± 0.04 µg/g) significantly exceeded those of BPA-fed mice (0.52 ± 0.05 µg/g) and thus provided greater tumor tissue boron enrichment for achieving a stronger neutron radiation-sensitizing effect. In conclusion, nucleotide borate esters, especially ATP borate ester, exhibited superior neutron radiosensitization effects than did other representative borate ester compounds and significantly greater long-term radiation effects as well. Thus, nucleotide borate esters have several advantages over other borate esters for BNCT and therefore warrant further study. [ABSTRACT FROM AUTHOR]

In order to effectively reduce the energy consumption of high sulfur natural gas purification process, in this paper, optimization model based on the genetic algorithm (GA) was developed. The natural gas purification process steady state model was established by using process simulation software ProMax. 8 key operating parameters of the purification system were determined by the process simulation model and energy consumption analysis. To reduce the calculation time and to solve the no convergence problems in the process simulation model, the BP (Back Propagation) neural network model was applied to train and test the simulated data. Then the BP model was incorporated into Genetic Algorithms to develop the energy consumption optimization model. A case study was performed in a high-sulfur natural gas purification plant with the capacity of 300×10 4 Nm 3 /d. And the results demonstrate that the energy consumption of the purification plant was reduced by 12.7%. [ABSTRACT FROM AUTHOR]

To understand the intrinsic influence of a drug on lipid membranes is of critical importance in pharmacological science. Herein, we report fluorescence microscopy analysis of the interaction between the local anesthetic tetracaine (TTC) and planar supported lipid bilayers (SLBs), as model membranes. Our results show that TTC increases lipid chain mobility, destabilizes the SLBs and remarkably induces membrane disruption and solubilization. Upon TTC binding, a local curvature change in the bilayer was observed, which led to the subsequent formation of up to 20-μm-long flexible lipid tubules as well as the formation of micron-size holes. Quantitative analysis revealed that membrane solubilization process can be divided into two distinct different stages as a function of TTC concentration. In the first stage (<800 μM), the bilayer disruption profiles fit well to a Langmuir isotherm, while in the second stage (800 μM–25 mM), TTC solubilizes the membrane in a detergent-like manner. Notably, the onset of membrane solubilization occurred below the critical micelle concentration (cmc) of TTC, indicating a local accumulation of the drug in the membrane. Additionally, cholesterol increases the insertion of TTC into the membrane and thus promotes the solubilization effect of TTC on lipid bilayers. These findings may help to elucidate the possible mechanisms of TTC interaction with lipid membranes, the dose dependent toxicity attributed to local anesthetics, as well as provide valuable information for drug development and modification. Unlabelled Image • Tetracaine destabilizes the supported lipid bilayers and remarkably induces membrane solubilization and disruption • Tetracaine induces changes in local membrane curvature, leading to the formation of lipid tubules and membrane holes • Tetracaine solubilizes phosphatidylcholine membranes in two distinct steps as a function of concentration • The solubilization effect of TTC on lipid bilayers can be modulated by cholesterol [ABSTRACT FROM AUTHOR]

Background: Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application., Methods: Patients with trunk melanoma analyzed in this study were from the SEER program [2010-2015]. The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram., Results: We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree., Conclusions: This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-6045/coif). The authors have no conflicts of interest to declare., (2022 Annals of Translational Medicine. All rights reserved.)