Coupling coordination evaluation and driving path of digital economy and carbon emission efficiency in China: A fuzzy-set qualitative comparative analysis based on 30 provinces.

Enhancing the level of coupling coordination between the digital economy (DIE) and carbon emission efficiency (CEE) is not only an inevitable choice for achieving the goals of energy conservation and emission reduction and promoting green development in China, but also a key path to implementing China's "Double Carbon" strategy. Based on the relevant statistical data of 30 provincial-level regions in China from the period covering 2011 to 2019, this paper empirically analyzed the coupling coordination between the DIE and CEE and its influencing factors. In this study, an improved coupling coordination degree (CCD) model was used to evaluate the degree of the coupling and coordinated development of the DIE and CEE in provincial regions of China. Finally, based on the Technology-Organization-Environment (TOE) framework, a fuzzy-set qualitative comparative analysis (fsQCA) method was employed to identify the realization path of the coupling and coordinated development of the DIE and CEE from the perspective of configuration. The results demonstrated that the coupling coordination between the DIE and CCE in China demonstrated a gradual upward trend, and exhibited regional differences, showing a decreasing trend of east > middle > west. Regarding the influencing factors, no single influencing factor could act as a necessary condition for the high CCD, the coupling and coordinated development of the DIE and CEE is a multifactorial synergy. There were five paths for the high degree of coupling coordination between the DIE and CEE, which were divided into three types: organization-environment-led type, environment-led type, and technology-organization-led type. Furthermore, technological innovation level and industrial structure could substitute for one another in some conditions, and environmental regulation and economic development level were synchronized. These conclusions provide a theoretical basis for countries to formulate policies to promote the coupling and coordinated development of their DIE and CEE

A Laguerre Neural Network-Based ADP Learning Scheme with its Application to Tracking Control in the Internet of Things

Sensory data have becoming widely available in large volume and variety due to the increasing presence and adoption of the Internet of Things. Such data can be tremendously useful if they are processed properly in a timely fashion. They could play a key role in the coordination of industrial production. It is thus desirable to explore an effective and efficient scheme to support data tracking and monitoring. This paper intends to propose a novel automatic learning scheme to improve the tracking efficiency while maintaining or improving the data tracking accuracy. A core strategy in the proposed scheme is the design of Laguerre neural network (LaNN)-based approximate dynamic programming (ADP). As a traditional optimal learning strategy, ADP is a popular approach for data processing. The action neural network (NN) and the critic NN as two important components in ADP have big impact on the performance of ADP. In this paper, a LaNN is employed as the implementation of the action NN in ADP considering Laguerre polynomials’ approximation capability. In addition, this LaNN-based ADP is integrated into an online parameter-tuning framework to optimize those parameters of characteristic model that is used to trace the data in the tracking control system. Meanwhile, this article provides an associated Lyapunov convergence analysis to guarantee a uniformly ultimately boundedness property for tracking errors in the proposed approach. Furthermore, the proposed LaNN-based ADP optimal online parameter-tuning scheme is validated using a temperature dynamic tracking control task. The simulation results demonstrate that the scheme has satisfactory learning performance over time

A Laguerre Neural Network-Based ADP Learning Scheme with its Application to Tracking Control in the Internet of Things

Sensory data have becoming widely available in large volume and variety due to the increasing presence and adoption of the Internet of Things. Such data can be tremendously useful if they are processed properly in a timely fashion. They could play a key role in the coordination of industrial production. It is thus desirable to explore an effective and efficient scheme to support data tracking and monitoring. This paper intends to propose a novel automatic learning scheme to improve the tracking efficiency while maintaining or improving the data tracking accuracy. A core strategy in the proposed scheme is the design of Laguerre neural network (LaNN)-based approximate dynamic programming (ADP). As a traditional optimal learning strategy, ADP is a popular approach for data processing. The action neural network (NN) and the critic NN as two important components in ADP have big impact on the performance of ADP. In this paper, a LaNN is employed as the implementation of the action NN in ADP considering Laguerre polynomials’ approximation capability. In addition, this LaNN-based ADP is integrated into an online parameter-tuning framework to optimize those parameters of characteristic model that is used to trace the data in the tracking control system. Meanwhile, this article provides an associated Lyapunov convergence analysis to guarantee a uniformly ultimately boundedness property for tracking errors in the proposed approach. Furthermore, the proposed LaNN-based ADP optimal online parameter-tuning scheme is validated using a temperature dynamic tracking control task. The simulation results demonstrate that the scheme has satisfactory learning performance over time

The Application of Hollow Fiber Cartridge in Biomedicine

The hollow fiber cartridge has the advantages of good semi-permeability, high surface area to volume ratio, convenient operation, and so on. Its application in chemical analysis, drug in vitro experiment, hemodialysis, and other fields has been deeply studied. This paper introduces the basic structure of hollow fiber cartridge, compares the advantages and disadvantages of a hollow fiber infection model constructed by a hollow fiber cartridge with traditional static model and animal infection model and introduces its application in drug effects, mechanism of drug resistance, and evaluation of combined drug regimen. The principle and application of hollow fiber bioreactors for cell culture and hollow fiber dialyzer for dialysis and filtration were discussed. The hollow fiber cartridge, whether used in drug experiments, artificial liver, artificial kidney, etc., has achieved controllable experimental operation and efficient and accurate experimental results, and will provide more convenience and support for drug development and clinical research in the future

Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug–drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies

Comparative Proteomic Analysis of Proteins in Breast Milk during Different Lactation Periods

Breast milk is an unparalleled food for infants, as it can meet almost all of their nutritional needs. Breast milk in the first month is an important source of acquired immunity. However, breast milk protein may vary with the stage of lactation. Therefore, the aim of this study was to use a data-independent acquisition approach to determine the differences in the proteins of breast milk during different lactation periods. The study samples were colostrum (3–6 days), transitional milk (7–14 days), and mature milk (15–29 days). The results identified a total of 2085 different proteins, and colostrum contained the most characteristic proteins. Protein expression was affected by the lactation stage. The proteins expressed in breast milk changed greatly between day 3 and day 14 and gradually stabilized after 14 days. The expression levels of lactoferrin, immunoglobulin, and clusterin were the highest in colostrum. CTP synthase 1, C-type lectin domain family 19 member A, secretoglobin family 3A member 2, trefoil factor 3 (TFF3), and tenascin were also the highest in colostrum. This study provides further insights into the protein composition of breast milk and the necessary support for the design and production of infant formula

Terahertz emission from vertically aligned multi-wall carbon nanotubes and their composites by optical excitation

International audienc

Iron Overload Impairs Bone Marrow Mesenchymal Stromal Cells from Higher-Risk MDS Patients by Regulating the ROS-Related Wnt/β-Catenin Pathway

The bone marrow microenvironment plays important roles in the progression of the myelodysplastic syndrome (MDS). The higher incidence of ASXL1 and TET2 gene mutations in our iron overload (IO) MDS patients suggests that IO may be involved in the pathogenesis of MDS. The effects of IO damaging bone marrow mesenchymal stromal cells (MSCs) from higher-risk MDS patients were investigated. In our study, IO decreased the quantity and weakened the abilities of proliferation and differentiation of MSCs, and it inhibited the gene expressions of VEGFA, CXCL12, and TGF-β1 in MSCs regulating hematopoiesis. The increased level of reactive oxygen species (ROS) in MSCs caused by IO might be inducing apoptosis by activating caspase3 signals and involving in MDS progression by activating β-catenin signals. The damages of MSCs caused by IO could be partially reversed by an antioxidant or an iron chelator. Furthermore, the MSCs in IO MDS/AML patients had increased levels of ROS and apoptosis, and the expressions of caspase3 and β-catenin were increased even further. In conclusion, IO affects gene stability in higher-risk MDS patients and impairs MSCs by inducing ROS-related apoptosis and activating the Wnt/β-catenin signaling pathway, which could be partially reversed by an antioxidant or an iron chelator

Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance

Antimicrobial resistance is a major public health issue. The pharmacokinetic/pharmacodynamic (PK/PD) model is an essential tool to optimize dosage regimens and alleviate the emergence of resistance. The semi-mechanistic PK/PD model is a mathematical quantitative tool to capture the relationship between dose, exposure, and response, in terms of the mechanism. Understanding the different resistant mechanisms of bacteria to various antibacterials and presenting this as mathematical equations, the semi-mechanistic PK/PD model can capture and simulate the progress of bacterial growth and the variation in susceptibility. In this review, we outline the bacterial growth model and antibacterial effect model, including different resistant mechanisms, such as persisting resistance, adaptive resistance, and pre-existing resistance, of antibacterials against bacteria. The application of the semi-mechanistic PK/PD model, such as the determination of PK/PD breakpoints, combination therapy, and dosage optimization, are also summarized. Additionally, it is important to integrate the PD effect, such as the inoculum effect and host response, in order to develop a comprehensive mechanism model. In conclusion, with the semi-mechanistic PK/PD model, the dosage regimen can be reasonably determined, which can suppress bacterial growth and resistance development