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A synthetic pathway to a novel 4-aryl-3,4-dihydro-2H-1,4-benzoxazine scaffold was developed and a series of compounds based on the scaffold were synthesised as potential anticancer agents. The 4-aryl-substituted compounds were prepared via Buchwald–Hartwig cross-coupling between substituted bromobenzenes and various 1,4-benzoxazines, which in turn were generated from a cascade hydrogenation and reductive amination one-pot reaction. These analogues exhibited moderate to good potency against various cancer cell lines. Structure–activity relationship analysis indicated that the inclusion of hydroxyl groups on ring A and ring B was beneficial to biological activity, while having a para-amino group on ring C significantly enhanced potency. Molecule 14f displayed the most potent anticancer activity (IC50 = 7.84–16.2 µM against PC-3, NHDF, MDA-MB-231, MIA PaCa-2, and U-87 MG cancer cell lines), indicating its potential as a lead compound for further structural optimisation. All the synthesised compounds were fully characterised with NMR, HMRS, and IR. The novel benzoxazine scaffold described in this study holds promise and deserves further in-depth studies. [ABSTRACT FROM AUTHOR]

Living organisms are capable of dynamically changing their structures for adaptive functions through sophisticated reaction‐diffusion processes. Here we show how active supramolecular hydrogels with programmable lifetimes and macroscopic structures can be created by relying on a simple reaction‐diffusion strategy. Two hydrogel precursors (poly(acrylic acid) PAA/CaCl2 and Na2CO3) diffuse from different locations and generate amorphous calcium carbonate (ACC) nanoparticles at the diffusional fronts, leading to the formation of hydrogel structures driven by electrostatic interactions between PAA and ACC nanoparticles. Interestingly, the formed hydrogels are capable of autonomously disintegrating over time because of a delayed influx of electrostatic‐interaction inhibitors (NaCl). The hydrogel growth process is well explained by a reaction‐diffusion model which offers a theoretical means to program the dynamic growth of structured hydrogels. Furthermore, we demonstrate a conceptual access to dynamic information storage in soft materials using the developed reaction‐diffusion strategy. This work may serve as a starting point for the development of life‐like materials with adaptive structures and functionalities. [ABSTRACT FROM AUTHOR]

Living organisms are capable of dynamically changing their structures for adaptive functions through sophisticated reaction‐diffusion processes. Here we show how active supramolecular hydrogels with programmable lifetimes and macroscopic structures can be created by relying on a simple reaction‐diffusion strategy. Two hydrogel precursors (poly(acrylic acid) PAA/CaCl2 and Na2CO3) diffuse from different locations and generate amorphous calcium carbonate (ACC) nanoparticles at the diffusional fronts, leading to the formation of hydrogel structures driven by electrostatic interactions between PAA and ACC nanoparticles. Interestingly, the formed hydrogels are capable of autonomously disintegrating over time because of a delayed influx of electrostatic‐interaction inhibitors (NaCl). The hydrogel growth process is well explained by a reaction‐diffusion model which offers a theoretical means to program the dynamic growth of structured hydrogels. Furthermore, we demonstrate a conceptual access to dynamic information storage in soft materials using the developed reaction‐diffusion strategy. This work may serve as a starting point for the development of life‐like materials with adaptive structures and functionalities. [ABSTRACT FROM AUTHOR]

Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management. [ABSTRACT FROM AUTHOR]

In this paper, we consider a nonlocal advection model for two populations on a bounded domain. The first part of the paper is devoted to the existence and uniqueness of solutions and the associated semi-flow properties. By employing the notion of solution integrated along the characteristics, we rigorously prove the segregation property of solutions. Furthermore, we construct an energy functional to investigate the asymptotic behavior of solutions. To resolve the lack of compactness of the positive orbits, we obtain a description of the asymptotic behavior of solutions by using the narrow convergence in the space of Young measures. The last section of the paper is devoted to numerical simulations, which confirm and complement our theoretical results. [ABSTRACT FROM AUTHOR]

Summary DelaySSAToolkit.jl is a Julia package for modelling reaction systems with non-Markovian dynamics, specifically those with time delays. These delays implicitly capture multiple intermediate reaction steps and hence serve as an effective model reduction technique for complex systems in biology, chemistry, ecology and genetics. The package implements a variety of exact formulations of the delay stochastic simulation algorithm. Availability and implementation The source code and documentation of DelaySSAToolkit.jl are available at https://github.com/palmtree2013/DelaySSAToolkit.jl. [ABSTRACT FROM AUTHOR]

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately. [ABSTRACT FROM AUTHOR]

Recently, the advancement of machine learning (ML) techniques, especially deep learning, reinforcement learning, and federated learning, has led to remarkable breakthroughs in a variety of application domains. The success of ML benefits from the advancement of the Internet, mobile networks, data center networks, and the Internet of Things (IoT) that facilitate data creation and sharing. On the other hand, we have also witnessed a fast growing trend in the networking community toward using ML to tackle challenging problems in network design, management, and optimization, which are traditionally addressed using mathematical optimization theory or human-generated heuristics. ML is also an essential ingredient in the realization of autonomous or self-driving networks. [ABSTRACT FROM AUTHOR]

Despite increased attention to the aquaculture environment, there is still a lack of understanding regarding the significance of water quality. To address this knowledge gap, this study utilized high-throughput sequencing of 16S rRNA and 18S rRNA to examine microbial communities (bacteria and eukaryotes) in coastal water over different months through long-term observations. The goal was to explore interaction patterns in the microbial community and identify potential pathogenic bacteria and red tide organisms. The results revealed significant differences in composition, diversity, and richness of bacterial and eukaryotic operational taxonomic units (OTUs) across various months. Principal coordinate analysis (PCoA) demonstrated distinct temporal variations in bacterial and eukaryotic communities, with significant differences (P = 0.001) among four groups: F (January-April), M (May), S (June–September), and T (October-December). Moreover, a strong association was observed between microbial communities and months, with most OTUs showing a distinct temporal preference. The Kruskal–Wallis test (P < 0.05) indicated significant differences in dominant bacterial and eukaryotic taxa among months, with each group exhibiting unique dominant taxa, including potential pathogenic bacteria and red tide organisms. These findings emphasize the importance of monitoring changes in potentially harmful microorganisms in aquaculture. Network analysis highlighted positive correlations between bacteria and eukaryotes, with bacteria playing a key role in network interactions. The key bacterial genera associated with other microorganisms varied significantly (P < 0.05) across different groups. In summary, this study deepens the understanding of aquaculture water quality and offers valuable insights for maintaining healthy aquaculture practices. Key points: • Bacterial and eukaryotic communities displayed distinct temporal variations. • Different months exhibited unique potential pathogenic bacteria and red tide organisms. • Bacteria are key taxonomic taxa involved in microbial network interactions. [ABSTRACT FROM AUTHOR]

Quantum entanglement brings high-speed and inherently privacy-preserving transmission for information communication in quantum networks. The qubit scarcity is an important issue that cannot be ignored in quantum networks due to the limited storage capacity of quantum devices, the short lifespan of qubits, and so on. In this article, we first formulate the qubit competition problem as the Cooperative-Qubit-Allocation-Problem (CQAP) by taking into account both the waiting time and the fidelity of end-to-end entanglement with the given transmission link set. We then model the CQAP as a Markov Decision Process (MDP) and adopt a Reinforcement Learning (RL) algorithm to self-adaptively and cooperatively allocate qubits among quantum repeaters. Further, we introduce an Active Learning (AL) algorithm to improve the efficiency of the RL algorithm by reducing its trial-error times. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithms, with 23.5 ms reduction on the average waiting time and 19.2 improvement on the average path maturity degree, respectively. [ABSTRACT FROM AUTHOR]

• Enzyme-like catalyst S/CuMgAL-41 with confined space was successfully constructed by coprecipitation and thiourea modification. • The S/CuMgAl-41 is a bifunctional catalyst with both acidic and alkaline active site. • NO 3 2− and SO 4 2− come from calcination of CuMgAL-41 modified by thiourea not only enhance the electrophilicity of aldehydes but also anchor of p -NBD with NO 3 2− and SO 4 2− by hydrogen, thereby increase the selectivity of the entantiomers and ee value. • The asymmetric aldol addition of p -NBD with acetone catalyzed S/CuMgAL-41 is the synergistic result of diverse factors such as the acid-base active center on the S/CuMgAL-41, hydrogen bonding, confined effect, anchoring effect. In this study, a series catalysts of CuMgAl-LDHs were successfully prepared by co-precipitation method. Subsequently, the catalyst S/CuMgAl-41 was prepared by modifying CuMgAl-41 with thiourea. The physical and chemical properties of the catalyst were characterized by XRD, N 2 adsorption-desorption, SEM, ICP, FT-IR, TG-DSC and XPS. The catalytic performance of the catalyst was investigated. It showed that S/CuMgAl-41 exhibited synergistic catalytic activity with better ee value due to both acidic and alkaline active sites and confined spaces with proper interlayer spacing. When isopropyl alcohol was chosen as solvent, the S/CuMgAl-41 has the best catalytic effect for asymmetric aldol addition reaction between p -nitrobenzaldehyde (p -NBD) and acetone. Additionally, the process conditions for optimizing the asymmetric addition reaction have been thoroughly studied. It was found that S/CuMgAl-41 exhibited good reusability and stability. Moreover, the asymmetric aldol addition of acetone and p -NBD catalyzed by S/CuMgAL-41 can occur due to the result of synergistic effects of various factors, including acid-base active center, hydrogen bond, confined effect and anchoring effects between hydrotalcite laminate of S/CuMgAL-41. This novel catalytic design provides a new idea for asymmetric synthesis of p -NBD and acetone. In this study, a series catalysts of CuMgAl-LDHs were successfully prepared by co-precipitation method. Subsequently, the catalyst S/CuMgAl-41 was prepared by modifying CuMgAl-41 with thiourea. The physical and chemical properties of the catalyst were characterized by XRD, N 2 adsorption-desorption, SEM, ICP, FT-IR, TG-DSC and XPS. The catalytic performance of the catalyst was investigated. It showed that S/CuMgAl-41 exhibited synergistic catalytic activity with better ee value due to both acidic and alkaline active sites and confined spaces with proper interlayer spacing. When isopropyl alcohol was chosen as solvent, the S/CuMgAl-41 has the best catalytic effect for asymmetric aldol addition reaction between p -nitrobenzaldehyde (p -NBD) and acetone. Additionally, the process conditions for optimizing the asymmetric addition reaction have been thoroughly studied. It was found that S/CuMgAl-41 exhibited good reusability and stability. Moreover, the asymmetric aldol addition of acetone and p -NBD catalyzed by S/CuMgAL-41 can occur due to the result of synergistic effects of various factors, including acid-base active center, hydrogen bond, confined effect and anchoring effects between hydrotalcite laminate of S/CuMgAL-41. This novel catalytic design provides a new idea for asymmetric synthesis of p -NBD and acetone. [Display omitted] [ABSTRACT FROM AUTHOR]

Predicting individual socioeconomic status (SES) from social media content benefits various applications in economic and social fields. Most previous works adopt machine learning methods with predefined features to infer SES. Nevertheless, they ignore some important information of social media content, such as order, structure and relation information, which leads to limited performance. In this paper, we propose a COupled social media content REpresentation model (CORE) for individual SES prediction, which efficiently exploits latent complex couplings of social media content. CORE devises a structure-aware social media text representation method to incorporate the order and the hierarchy of social media text, and leverages a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. Our experiments on a real data set of a Chinese microblogging platform demonstrate that our approach significantly outperforms benchmark methods, which validates its efficiency and robustness. The proposed model could be applied to improve the SES prediction and other user profiling tasks. • A coupled social media content representation model for predicting individual SES is proposed. • A structure-aware social media text representation method is presented. • A coupled attribute representation method exploring intrinsic relationships is devised. • Experiments prove that the proposed model greatly outperforms state-of-the-art models. [ABSTRACT FROM AUTHOR]

Background: Choline is a dietary precursor to the gut microbial generation of the prothrombotic and proatherogenic metabolite trimethylamine-N-oxide (TMAO). Eggs are rich in choline, yet the impact of habitual egg consumption on TMAO levels and platelet function in human subjects remains unclear.Methods: Healthy volunteers (41% male, 81% Caucasian, median age 28 years) with normal renal function (estimated glomerular filtration rate >60) were recruited and assigned to 1 of 5 daily interventions for 4 weeks: 1) hardboiled eggs (n = 18); 2) choline bitartrate supplements (n = 20); 3) hardboiled eggs + choline bitartrate supplements (n = 16); 4) egg whites + choline bitartrate supplements (n = 18); 5) phosphatidylcholine supplements (n = 10). Fasting blood and urine samples were collected for quantification of TMAO, its precursors, and platelet aggregometry.Results: Participants' plasma TMAO levels increased significantly in all 3 intervention arms containing choline bitartrate (all P < .0001), but daily ingestion of 4 large eggs (P = .28) or phosphatidylcholine supplements (P = .27) failed to increase plasma TMAO levels. Platelet reactivity also significantly increased in the 3 intervention arms containing choline bitartrate (all P < .01), but not with eggs (P = .10) or phosphatidylcholine supplements (P = .79).Conclusions: Despite high choline content in egg yolks, healthy participants consuming 4 eggs daily showed no significant increase in TMAO or platelet reactivity. However, choline bitartrate supplements providing comparable total choline raised both TMAO and platelet reactivity, demonstrating that the form and source of dietary choline differentially contributes to systemic TMAO levels and platelet responsiveness. [ABSTRACT FROM AUTHOR]