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36896496 | A New Monoterpene Alkaloid from Incarvillea sinensis with Migration Inhibitory Activity on Cancer Cell. | A novel monoterpene alkaloid, named incarvine G, was isolated from the Incarvillea sinensis Lam. Its chemical structure was elucidated prehensive spectroscopic methods. Incarvine G is an prised of a monoterpene alkaloid and glucose. pound showed evident inhibition on cell migration, invasion, and cytoskeleton formation of human MDA-MB-231 with low cytotoxicity. |
36896497 | Chemical profiling of Ziziphi spinosae semen using on-line comprehensive two-dimensional liquid chromatography-mass spectrometry based on a novel phthalic anhydride bonded stationary phase. | Ziziphi spinosae semen has been widely used to treat insomnia and anxiety. To profile its ponents, an prehensive two-dimensional liquid chromatography-mass spectrometry was developed. In this two-dimensional liquid chromatography system, a novel phthalic anhydride-bonded stationary phase column bined with a C18 column. As a result, this new stationary phase exhibited remarkable differences in separation selectivity from C18, achieving a good orthogonality of 83.3%. Moreover, this new stationary phase with weaker hydrophobicity than C18 realized patibility in the online configuration. Coupled with tandem MS, pounds were identified, including 51 pounds. Compared with one-dimensional liquid chromatography-mass spectrometry, this online two-dimensional liquid chromatography-mass spectrometry system exhibited a much higher resolving power in isomer separation. This work provided an effective separation and characterization method for the material basis of Ziziphi spinosae semen. This strategy provides ideas for the material basis research of other traditional Chinese medicines. |
36896499 | Hierarchical Serpentine-Helix Combination for 3D Stretchable Electronics. | 3D stretchable electronics attract growing interest due to their new and plex pared to 1D or 2D counterparts. Among all 3D configuration designs, a 3D helical structure monly used as it can be designed to achieve outstanding stretching ratios as well as highly robust mechanical performance. However, the stretching ratio that mainly focuses on the axis direction hinders its applications. Inspired by hierarchies in a tendon, a novel structural design of hierarchical 3D bination is proposed. The structural design constructed by a sequence with repeating small units winding in a helical manner around the axis can enable large mechanical forces transferred down to a smaller scale with the dissipation of potentially damaging stresses by microscale buckling, thereby endowing the ponents made from high-performance but hard-to-stretch materials with large stretchability (≥200%) in x-, y-, or z-axis direction, high structural stability, and extraordinary electromechanical performance. Two applications including a wireless charging patch and an epidermal electronic system are demonstrated. The epidermal electronic system made of several hierarchical 3D binations allows for high-fidelity monitoring of electrophysiological signals, galvanic skin response, and finger-movement-induced electrical signals, which can achieve good tactile pattern recognition bined with an artificial neural network. |
36896498 | A dielectrophoresis-based platform of cancerous cell capture using aptamer-functionalized gold nanoparticles in a microfluidic channel. | In this paper, a microfluidic chip for the manipulation and capture of cancer cells was introduced, in which bination of dielectrophoresis (DEP) and a binding method based on chemical interactions by using cell-specific aptamers was performed to enhance the capture strength and specificity. The device has been simply constructed from a straight-channel PDMS placed on a glass substrate that has patterned electrode structures and a self-assembled monolayer of gold nanoparticles (AuNPs). The target cells were transported to the manipulation area by flow and attracted down to the region between the electrodes under the influence of positive DEP force. This approach facilitated subsequent selective capture by the modified aptamers on the AuNPs. The distribution of the electric field in the channel has also been simulated to clarify the DEP operation. As a result, the device has been shown to effectively capture target lung cancer cells with a concentration as low as |
36896501 | Training Health Care Practitioners to Include Family Caregivers With Web-Based Learning Modules. | Background Caregivers play a key role in supporting patient health; however, they have largely been excluded from participating in health care teams. This paper describes development and evaluation of web-based training for health care professionals about including family caregivers, implemented within the Department of Veterans Affairs Veterans Health Administration. Systematically training health care professionals constitutes a critical step toward shifting to a culture of purposefully and effectively utilizing and supporting family caregivers for better patient and health system es. Methods Module development included Department of Veterans Affairs health care stakeholders and consisted of preliminary research and a design approach to set the framework, followed by iterative, collaborative team processes to write the content. Evaluation included pre- and postassessments of knowledge, attitudes, and beliefs. Results Overall, 154 health pleted pretest questions and 63 pleted the posttest. There was no observable change in knowledge. However, participants indicated a perceived desire and need for practicing inclusive care as well as an increase in self-efficacy (belief in their ability to plish a task successfully under certain conditions). Conclusion This project demonstrates the feasibility of developing web-based training to improve the beliefs and attitudes of health care professionals about inclusive care. Training constitutes one step toward shifting to a culture of inclusive care, and research should identify longer-term effects and other evidence-based interventions. |
36896500 | Online Fully Automated System for Hydrogen/Deuterium-Exchange Mass Spectrometry with Millisecond Time Resolution. | Amide hydrogen/deuterium-exchange mass spectrometry (HDX-MS) is a powerful tool for analyzing the conformational dynamics of proteins in a solution. Current conventional methods have a measurement limit starting from several seconds and are solely reliant on the speed of manual pipetting or a liquid handling robot. Weakly protected regions of polypeptides, such as in short peptides, exposed loops and intrinsically disordered the protein exchange on the millisecond timescale. Typical HDX methods often cannot resolve the structural dynamics and stability in these cases. Numerous academic laboratories have demonstrated the considerable utility of acquiring HDX-MS data in the sub-second regimes. Here, we describe the development of a fully automated HDX-MS apparatus to resolve amide exchange on the millisecond timescale. Like conventional systems, this instrument boasts automated sample injection with software selection of labeling times, online flow mixing and quenching, while being fully integrated with a liquid chromatography-MS system for existing standard "bottom-up" workflows. HDX-MS's rapid exchange kinetics of several peptides demonstrate the repeatability, reproducibility, back-exchange, and mixing kinetics achieved with the system. Comparably, peptide coverage of 96.4% with 273 peptides was achieved, supporting the equivalence of the system to standard robotics. Additionally, time windows of 50 ms-300 s allowed full kinetic transitions to be observed for many amide groups; especially important are short time points (50-150 ms) for regions that are likely highly dynamic and solvent- exposed. We demonstrate that information on structural dynamics and stability can be measured for stretches of weakly stable polypeptides in small peptides and in local regions of a large enzyme, glycogen phosphorylase. |
36896502 | Constrained Langevin approximation for the Togashi-Kaneko model of autocatalytic reactions. | The Togashi Kaneko model (TK model) is a simple stochastic reaction network that displays discreteness-induced transitions between meta-stable patterns. Here we study a constrained Langevin approximation (CLA) of this model. This CLA, derived under the classical scaling, is an obliquely reflected diffusion process on the positive orthant and hence respects the constraint that chemical concentrations are never negative. We show that the CLA is a Feller process, is positive Harris recurrent and converges exponentially fast to the unique stationary distribution. We also characterize the stationary distribution and show that it has finite moments. In addition, we simulate both the TK model and its CLA in various dimensions. For example, we describe how the TK model switches between meta-stable patterns in dimension six. Our simulations suggest that, when the volume of the vessel in which all of the reactions that take place is large, the CLA is a good approximation of the TK model in terms of both the stationary distribution and the transition times between patterns. |
36896503 | Immersive virtual reality application for intelligent manufacturing: Applications and art design. | Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users municate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done bine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered. |
36896504 | Study of visual SLAM methods in minimally invasive surgery. | In recent years, minimally invasive surgery has developed rapidly in the clinical practice of surgery and has gradually e one of the critical surgical techniques. Compared with traditional surgery, the advantages of minimally invasive surgery include small incisions and less pain during the operation, and the patients recover faster after surgery. With the expansion of minimally invasive surgery in several medical fields, traditional minimally invasive techniques have bottlenecks in clinical practice, such as the inability of the endoscope to determine the depth information of the lesion area from the two-dimensional images obtained, the difficulty in locating the endoscopic position information and the inability to get plete view of the overall situation in the cavity. This paper uses a visual simultaneous localization and mapping (SLAM) approach to achieve endoscope localization and reconstruction of the surgical region in a minimally invasive surgical environment. Firstly, the K-Means bined with the Super point algorithm is used to extract the feature information of the image in the lumen environment. Compared with Super points, the logarithm of successful matching points increased by 32.69%, the proportion of effective points increased by 25.28%, the error matching rate decreased by 0.64%, and the extraction time decreased by 1.98%. Then the iterative closest point method is used to estimate the position and attitude information of the endoscope. Finally, the disparity map is obtained by the stereo matching method, and the point cloud image of the surgical area is finally recovered. |
36896505 | Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm. | In order to enhance puted tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR pared. The five evaluation indices, the mean squared error (MSE), mutual information, Pearson correlation coefficient (PCC), structural similarity index (SSIM), and PCC + SSIM, were used to validate the results of registration with SR. Moreover, the proposed method SR-DLDR was pared with the VoxelMorph (VM) method. In rigid registration with SR, the registration accuracy improved by up to 6% in the PCC metric. In DLDR with SR, the registration accuracy was improved by up to 5% in PCC + SSIM. When taking the MSE as the loss function, the accuracy of SR-DLDR is equivalent to that of the VM method. In addition, when taking the SSIM as the loss function, the registration accuracy of SR-DLDR is 6% higher than that of VM. SR is a feasible method to be used in medical image registration for planning CT (pCT) and CBCT. The experimental results show that the SR algorithm can improve the accuracy and efficiency of CBCT image alignment regardless of which alignment algorithm is used. |
36896506 | A periodic boundary value problem of fractional differential equation involving p(t)-Laplacian operator. | The purpose of this article is to research the existence of solutions for fractional periodic boundary value problems with p(t)-Laplacian operator. In this regard, the article needs to establish a continuation theorem corresponding to the above problem. By applying the continuation theorem, a new existence result for the problem is obtained, which enriches existing literature. In addition, we provide an example to verify the main result. |
36896507 | Existence results of fractional differential equations with nonlocal double-integral boundary conditions. | This article presents the existence es concerning a family of singular nonlinear differential equations containing Caputo's fractional derivatives with nonlocal double integral boundary conditions. According to the nature of Caputo's fractional calculus, the problem is converted into an equivalent integral equation, while two standard fixed theorems are employed to prove its uniqueness and existence results. An example is presented at the end of this paper to illustrate our obtained results. |
36896508 | Bayesian parameter estimation for phosphate dynamics during hemodialysis. | Hyperphosphatemia in patients with renal failure is associated with increased vascular calcification and mortality. Hemodialysis is a conventional treatment for patients with hyperphosphatemia. Phosphate kinetics during hemodialysis may be described by a diffusion process and modeled by ordinary differential equations. We propose a Bayesian model approach for estimating patient-specific parameters for phosphate kinetics during hemodialysis. The Bayesian approach allows us to both analyze the full parameter space using uncertainty quantification and pare two types of hemodialysis treatments, the conventional single-pass and the novel multiple-pass treatment. We validate and test our models on synthetic and real data. The results show limited identifiability of the model parameters when only single-pass data are available, and that the Bayesian model greatly reduces the relative standard pared to existing estimates. Moreover, the analysis of the Bayesian models reveal improved estimates with reduced uncertainty when considering consecutive sessions and multiple-pass pared to single-pass treatment. |
36896509 | A pseudospectral method for investigating the stability of linear population models with two physiological structures. | The asymptotic stability of the null equilibrium of a linear population model with two physiological structures formulated as a first-order hyperbolic PDE is determined by the spectrum of its infinitesimal generator. In this paper, we propose a general numerical method to approximate this spectrum. In particular, we first reformulate the problem in the space of absolutely continuous functions in the sense of Carathéodory, so that the domain of the corresponding infinitesimal generator is defined by trivial boundary conditions. Via bivariate collocation, we discretize the reformulated operator as a finite-dimensional matrix, which can be used to approximate the spectrum of the original infinitesimal generator. Finally, we provide test examples illustrating the converging behavior of the approximated eigenvalues and eigenfunctions, and its dependence on the regularity of the model coefficients. |
36896510 | Effect of decay behavior of information on disease dissemination in multiplex network. | The diseases dissemination always brings serious problems in the economy and livelihood issues. It is necessary to study the law of disease dissemination from multiple dimensions. Information quality about disease prevention has a great impact on the dissemination of disease, that is because only the real information can inhibit the dissemination of disease. In fact, the dissemination of information involves the decay of the amount of real information and the information quality es poor gradually, which will affect the individual's attitude and behavior towards disease. In order to study the influence of the decay behavior of information on disease dissemination, in the paper, an interaction model between information and disease dissemination is established to describe the effect of the decay behavior of information on the coupled dynamics of process in multiplex network. According to the mean-field theory, the threshold condition of disease dissemination is derived. Finally, through theoretical analysis and numerical simulation, some results can be obtained. The results show that decay behavior is a factor that greatly affects the disease dissemination and can change the final size of disease dissemination. The larger the decay constant, the smaller final size of disease dissemination. In the process of information dissemination, emphasizing key information can reduce the impact of decay behavior. |
36896511 | Regularization effect of the mixed-type damping in a higher-dimensional logarithmic Keller-Segel system related to crime modeling. | We study a logarithmic Keller-Segel system proposed by Rodríguez for crime modeling as follows: $ \begin{equation*} \left\{ \begin{split} &u_t = \Delta u-\chi\nabla\cdot\left(u\nabla\ln v\right)- \kappa uv+ h_1,\\ &v_t = \Delta v- v+ u+h_2, \end{split} \right. \end{equation*} $ in a bounded and smooth spatial domain $ \Omega\subset \mathbb R^n $ with $ n\geq3 $, with the parameters $ \chi > 0 $ and $ \kappa > 0 $, and with the nonnegative functions $ h_1 $ and $ h_2 $. For the case that $ \kappa = 0 $, $ h_1\equiv0 $ and $ h_2\equiv0 $, recent results showed that the corresponding initial-boundary value problem admits a global generalized solution provided that $ \chi < \chi_0 $ with some $ \chi_0 > 0 $. In the present work, our first result shows that for the case of $ \kappa > 0 $ such problem possesses global generalized solutions provided that $ \chi < \chi_1 $ with some $ \chi_1 > \chi_0 $, which seems to confirm that the mixed-type damping $ -\kappa uv $ has a regularization effect on solutions. Besides the existence result for generalized solutions, a statement on the large-time behavior of such solutions is derived as well. |
36896512 | Multi-source online transfer algorithm based on source domain selection for EEG classification. | The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing puter interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm. |
36896513 | Prediction of coronary heart disease in gout patients using machine learning models. | Growing evidence shows that there is an increased risk of cardiovascular diseases among gout patients, especially coronary heart disease (CHD). Screening for CHD in gout patients based on simple clinical factors is still challenging. Here we aim to build a diagnostic model based on machine learning so as to avoid missed diagnoses or over exaggerated examinations as much as possible. Over 300 patient samples collected from Jiangxi Provincial People's Hospital were divided into two groups (gout and gout+CHD). The prediction of CHD in gout patients has thus been modeled as a binary classification problem. A total of eight clinical indicators were selected as features for machine learning classifiers. bined sampling technique was used to e the imbalanced problem in the training dataset. Eight machine learning models were used including logistic regression, decision tree, ensemble learning models (random forest, XGBoost, LightGBM, GBDT), support vector machine (SVM) and neural networks. Our results showed that stepwise logistic regression and SVM achieved more excellent AUC values, while the random forest and XGBoost models achieved more excellent performances in terms of recall and accuracy. Furthermore, several high-risk factors were found to be effective indices in predicting CHD in gout patients, which provide insights into the clinical diagnosis. |
36896514 | Study on 4D taxiing path planning of aircraft based on spatio-temporal network. | In recent years, China vigorously develops energy conservation and emission reduction, in order to actively respond to the national call to make the aircraft operation process reduce unnecessary costs and strengthen the safety of the aircraft taxiing process. This paper studies the spatio-temporal network model and dynamic planning algorithm to plan the aircraft taxiing path. First, the relationship between the force, thrust and engine fuel consumption rate during aircraft taxiing is analyzed to determine the fuel consumption rate during aircraft taxiing. Then, a two-dimensional directed graph of airport network nodes is constructed. The state of the aircraft is recorded when considering the dynamic characteristics of the node sections, the taxiing path is determined for the aircraft using dijkstra's algorithm, and the overall taxiing path is discretized from node to node using dynamic planning to design a mathematical model with the shortest taxiing distance as the goal. At the same time, the optimal taxiing path is planned for the aircraft in the process of avoiding aircraft conflicts. Thus, a state-attribute-space-time field taxiing path network is established. Through example simulations, simulation data are finally obtained to plan conflict-free paths for six aircraft, the total fuel consumption for the six aircraft planning is 564.29 kg, and the total taxiing time is 1765s. pleted the validation of the dynamic planning algorithm of the spatio-temporal network model. |
36896515 | Methods to find strength of job competition among candidates under single-valued neutrosophic soft model. | Neutrosophic soft set theory is one of the most developed interdisciplinary research areas, with multiple applications in various fields such putational intelligence, applied mathematics, social networks, and decision science. In this research article, we introduce the powerful framework of single-valued neutrosophic petition graphs by integrating the powerful technique of single-valued neutrosophic soft set petition graph. For dealing with different levels petitive relationships among objects in the presence of parametrization, the novel concepts are defined which include single-valued neutrosophic soft petition graphs and petition single-valued neutrosophic soft graphs. Several energetic consequences are presented to obtain strong edges of the above-referred graphs. The significance of these novel concepts is investigated through application in petition and also an algorithm is developed to address this decision-making problem. |
36896516 | A numerical study of COVID-19 epidemic model with vaccination and diffusion. | The coronavirus infectious disease (or COVID-19) is a severe respiratory illness. Although the infection incidence decreased significantly, still it remains a major panic for human health and the global economy. The spatial movement of the population from one region to another remains one of the major causes of the spread of the infection. In the literature, most of the COVID-19 models have been constructed with only temporal effects. In this paper, a vaccinated spatio-temporal COVID-19 mathematical model is developed to study the impact of vaccines and other interventions on the disease dynamics in a spatially heterogeneous environment. Initially, some of the basic mathematical properties including existence, uniqueness, positivity, and boundedness of the diffusive vaccinated models are analyzed. The model equilibria and the basic reproductive number are presented. Further, based upon the uniform and non-uniform initial conditions, the spatio-temporal COVID-19 mathematical model is solved numerically using finite difference operator-splitting scheme. Furthermore, detailed simulation results are presented in order to visualize the impact of vaccination and other model key parameters with and without diffusion on the pandemic incidence. The obtained results reveal that the suggested intervention with diffusion has a significant impact on the disease dynamics and its control. |
36896518 | Mixing times for two classes of stochastically modeled reaction networks. | The past few decades have seen robust research on questions regarding the existence, form, and properties of stationary distributions of stochastically modeled reaction networks. When a stochastic model admits a stationary distribution an important practical question is: what is the rate of convergence of the distribution of the process to the stationary distribution? With the exception of [1] pertaining to models whose state space is restricted to the non-negative integers, there has been a notable lack of results related to this rate of convergence in the reaction network literature. This paper begins the process of filling that hole in our understanding. In this paper, we characterize this rate of convergence, via the mixing times of the processes, for two classes of stochastically modeled reaction networks. Specifically, by applying a Foster-Lyapunov criteria we establish exponential ergodicity for two classes of reaction networks introduced in [2]. Moreover, we show that for one of the classes the convergence is uniform over the initial state. |
36896517 | Estimating the time-dependent effective reproduction number and vaccination rate for COVID-19 in the USA and India. | The effective reproduction number, $ R_t $, is a vital epidemic parameter utilized to judge whether an epidemic is shrinking, growing, or holding steady. The main goal of this paper is to estimate bined $ R_t $ and time-dependent vaccination rate for COVID-19 in the USA and India after the vaccination campaign started. Accounting for the impact of vaccination into a discrete-time stochastic augmented SVEIR (Susceptible-Vaccinated-Exposed-Infectious-Recovered) model, we estimate the time-dependent effective reproduction number $ (R_t) $ and vaccination rate $ (\xi_t) $ for COVID-19 by using a low pass filter and the Extended Kalman Filter (EKF) approach for the period February 15, 2021 to August 22, 2022 in India and December 13, 2020 to August 16, 2022 in the USA. The estimated $ R_t $ and $ \xi_t $ show spikes and serrations with the data. Our forecasting scenario represents the situation by December 31, 2022 that the new daily cases and deaths are decreasing for the USA and India. We also noticed that for the current vaccination rate, $ R_t $ would remain greater than one by December 31, 2022. Our results are beneficial for the policymakers to track the status of the effective reproduction number, whether it is greater or less than one. As restrictions in these countries ease, it is still important to maintain safety and preventive measures. |
36896519 | Effect of nutrient supply on cell size evolution of marine phytoplankton. | The variation of nutrient supply not only leads to the differences in the phytoplankton biomass and primary productivity but also induces the long-term phenotypic evolution of phytoplankton. It is widely accepted that marine phytoplankton follows Bergmann's Rule and es smaller with climate warming. Compared with the direct effect of increasing temperature, the indirect effect via nutrient supply is considered to be an important and dominant factor in the reduction of phytoplankton cell size. In this paper, a size-dependent nutrient-phytoplankton model is developed to explore the effects of nutrient supply on the evolutionary dynamics of functional traits associated with phytoplankton size. The ecological reproductive index is introduced to investigate the impacts of input nitrogen concentration and vertical mixing rate on the persistence of phytoplankton and the distribution of cell size. In addition, by applying the adaptive dynamics theory, we study the relationship between nutrient input and the evolutionary dynamics of phytoplankton. The results show that input nitrogen concentration and vertical mixing rate have significant effects on the cell size evolution of phytoplankton. Specifically, cell size tends to increase with the input nutrient concentration, as does the diversity of cell sizes. In addition, a single-peaked relationship between vertical mixing rate and cell size is observed. When the vertical mixing rate is too low or too high, only small individuals are dominant in the water column. When the vertical mixing rate is moderate, large individuals can coexist with small individuals, so the diversity of phytoplankton is elevated. We predict that reduced intensity of nutrient input due to climate warming will lead to a trend towards smaller cell size and will reduce the diversity of phytoplankton. |
36896520 | Research on deep learning garbage classification system based on fusion of image classification and object detection classification. | With the development of national economy, the output of waste is also increasing. People's living standards are constantly improving, and the problem of garbage pollution is increasingly serious, which has a great impact on the environment. Garbage classification and processing has e the focus of today. This topic studies the garbage classification system based on deep learning convolutional neural network, which integrates the garbage classification and recognition methods of image classification and object detection. First, the data sets and data labels used are made, and then the garbage classification data are trained and tested through ResNet and MobileNetV2 algorithms, Three algorithms of YOLOv5 family are used to train and test garbage object data. Finally, five research results of garbage classification are merged. Through consensus voting algorithm, the recognition rate of image classification is improved to 2%. Practice has proved that the recognition rate of garbage image classification has been increased to about 98%, and it has been transplanted to the raspberry pie puter to achieve ideal results. |
36896521 | Dynamic analysis of the discrete fractional-order Rulkov neuron map. | Human evolution is carried out by two genetic systems based on DNA and another based on the transmission of information through the functions of the nervous system. putational neuroscience, mathematical neural models are used to describe the biological function of the brain. Discrete-time neural models have received particular attention due to their simple analysis and putational costs. From the concept of neuroscience, discrete fractional order neuron models incorporate the memory in a dynamic model. This paper introduces the fractional order discrete Rulkov neuron map. The presented model is analyzed dynamically and also in terms of synchronization ability. First, the Rulkov neuron map is examined in terms of phase plane, bifurcation diagram, and Lyapunov exponent. The biological behaviors of the Rulkov neuron map, such as silence, bursting, and chaotic firing, also exist in its discrete fractional-order version. The bifurcation diagrams of the proposed model are investigated under the effect of the neuron model's parameters and the fractional order. The stability regions of the system are theoretically and numerically obtained, and it is shown that increasing the order of the fractional order decreases the stable areas. Finally, the synchronization behavior of two fractional-order models is investigated. The results represent that the fractional-order systems cannot plete synchronization. |
36896522 | Linear barycentric rational collocation method for solving generalized Poisson equations. | We consider the Poisson equation by collocation method with linear barycentric rational function. The discrete form of the Poisson equation was changed to matrix form. For the basis of barycentric rational function, we present the convergence rate of the linear barycentric rational collocation method for the Poisson equation. Domain position method of the barycentric rational collocation method (BRCM) is also presented. Several numerical examples are provided to validate the algorithm. |
36896523 | Design of intelligent robots for tourism management service based on green computing. | The modular intelligent robot platform has important application prospects in the field of tourism management services. Based on the intelligent robot in the scenic area, this paper constructs a partial differential analysis system for tourism management services, and adopts the modular design method plete the hardware design of the intelligent robot system. Through system analysis, the whole system is divided into 5 major modules, including core control module, power supply module, motor control module, sensor measurement module, wireless sensor network module, to solve the problem of quantification of tourism management services. In the simulation process, the hardware development of wireless sensor network node is carried out based on MSP430F169 microcontroller and CC2420 radio frequency munication chip, and the corresponding physical layer and MAC (Media Access Control) layer data definition and data definition of IEEE802.15.4 protocol pleted for software implementation, and data transmission and networking verification. The experimental results show that the encoder resolution is 1024P/R, the power supply voltage is DC5V5%, and the maximum response frequency is 100 kHz. The algorithm designed by MATLAB software can avoid the existing ings and meet the real-time requirements of the system, which significantly improves the sensitivity and robustness of the intelligent robot. |
36896524 | Effects of vaccination on mitigating COVID-19 outbreaks: a conceptual modeling approach. | This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose partmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model |
36896525 | Multi-stage hybrid evolutionary algorithm for multiobjective distributed fuzzy flow-shop scheduling problem. | In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of pletion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages. In the first stage, the hybrid sampling strategy makes the population rapidly converge toward the Pareto front (PF) in multiple directions. In the second stage, the sequence difference-based differential evolution (SDDE) is used to speed up the convergence speed to improve the convergence performance. In the last stage, the evolutional direction of SDDE is changed to guide individuals to search the local area of the PF, thereby further improving the convergence and distribution performance. The results of experiments show that the performance of MSHEA-SDDE is superior to the parison algorithms in terms of solving the DFFSP. |
36896526 | Improved decay of solution for strongly damped nonlinear wave equations. | In this work, we deal with the initial boundary value problem of solutions for a class of linear strongly damped nonlinear wave equations $ u_{tt}-\Delta u -\alpha \Delta u_t = f(u) $ in the frame of a family of potential wells. For this strongly damped wave equation, we not only prove the global-in-time existence of the solution, but we also improve the decay rate of the solution from the polynomial decay rate to the exponential decay rate. |
36896527 | Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM. | Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least pared with existing methods. |
36896528 | IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation. | Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician's diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the first step, the algorithm designed the gland segmentation evaluation function. Then a new mutation strategy is established, and the adaptive controlled variables are used to balance the ability of improved differential evolution (IDE) in terms of investigation and convergence. To evaluate its performance, The proposed method is validated on a number of benchmark breast images, including four types of glands from the Quanzhou First Hospital, Fujian, China. Furthermore, the proposed algorithm is been pared to five state-of-the-art algorithms. From the average MSSIM and boxplot, the evidence suggests that the mutation strategy may be effective in searching the topography of the segmented gland problem. The experiment results demonstrated that the proposed method has the best gland segmentation pared to other algorithms. |
36896529 | Dual-process system based on mixed semantic fusion for Chinese medical knowledge-based question answering. | Chinese medical knowledge-based question answering (cMed-KBQA) is a ponent of the intelligence question-answering assignment. Its purpose is to enable the model prehend questions and then deduce the proper answer from the knowledge base. Previous methods solely considered how questions and knowledge base paths were represented, disregarding their significance. Due to entity and path sparsity, the performance of question and answer cannot be effectively enhanced. To address this challenge, this paper presents a structured methodology for the cMed-KBQA based on the cognitive science dual systems theory by synchronizing an observation stage (System 1) and an expressive reasoning stage (System 2). System 1 learns the question's representation and queries the associated simple path. Then System 2 plicated paths for the question from the knowledge base by using the simple path provided by System 1. Specifically, System 1 is implemented by the entity extraction module, entity linking module, simple path retrieval module, and simple path-matching model. Meanwhile, System 2 is performed by using plex path retrieval module plex path-matching model. The public CKBQA2019 and CKBQA2020 datasets were extensively studied to evaluate the suggested technique. Using the metric average F1-score, our model achieved 78.12% on CKBQA2019 and 86.60% on CKBQA2020. |
36896530 | Research on the evaluation method of steam power system operation status based on the theory of deterioration degree and health value. | The evaluation of the steam power system is very important for the operator to understand the operating status of the system, but the lack of consideration of the fuzziness of plex system and the impact of the indicator parameters on the whole system makes the evaluation difficult. In this paper, an indicator system for evaluating the operation status of the experimental supercharged boiler is established. After discussing several methods of parameter standardization and weight correction, prehensive evaluation method based on the deterioration degree and health value is proposed while considering the deviation of the indicator and the fuzziness of the system. prehensive evaluation method, the linear weighting method and the prehensive evaluation method are respectively used to evaluate the experimental supercharged boiler. parison of the three methods shows that prehensive evaluation method is more sensitive to minor anomalies and faults and can draw quantitative health assessment conclusions. |
36896531 | 3D human pose detection using nano sensor and multi-agent deep reinforcement learning. | Due to plexity of three-dimensional (3D) human pose, it is difficult for ordinary sensors to capture subtle changes in pose, resulting in a decrease in the accuracy of 3D human pose detection. A novel 3D human motion pose detection method is designed bining Nano sensors and multi-agent deep reinforcement learning technology. First, Nano sensors are placed in key parts of the human to collect human electromyogram (EMG) signals. Second, after de-noising the EMG signal by blind source separation technology, the time-domain and frequency-domain features of the surface EMG signal are extracted. Finally, in the multi-agent environment, the deep reinforcement learning network is introduced to build the multi-agent deep reinforcement learning pose detection model, and the 3D local pose of the human is output according to the features of the EMG signal. The fusion and pose calculation of the multi-sensor pose detection results are performed to obtain the 3D human pose detection results. The results show that the proposed method has high accuracy for detecting various human poses, and the accuracy, precision, recall and specificity of 3D human pose detection results are 0.97, 0.98, 0.95 and 0.98, respectively. Compared with other methods, the detection results in this paper are more accurate, and can be widely used in medicine, film, sports and other fields. |
36896532 | Continuous extraction of coronary artery centerline from cardiac CTA images using a regression-based method. | Coronary artery centerline extraction in puted tomography angiography (CTA) is an effectively non-invasive method to diagnose and evaluate coronary artery disease (CAD). The traditional method of manual centerline extraction is time-consuming and tedious. In this study, we propose a deep learning algorithm that continuously extracts coronary artery centerlines from CTA images using a regression method. In the proposed method, a CNN module is trained to extract the features of CTA images, and then the branch classifier and direction predictor are designed to predict the most possible direction and lumen radius at the given centerline point. Besides, a new loss function is developed for associating the direction vector with the lumen radius. The whole process starts from a point manually placed at the coronary artery ostia, and terminates until tracking the vessel endpoint. The network was trained using a training set consisting of 12 CTA images and the evaluation was performed using a testing set consisting of 6 CTA images. The extracted centerlines had an average overlap (OV) of 89.19%, overlap until first error (OF) of 82.30%, and overlap with clinically relevant vessel (OT) of 91.42% with manually annotated reference. Our proposed method can efficiently deal with multi-branch problems and accurately detect distal coronary arteries, thereby providing potential help in assisting CAD diagnosis. |
36896533 | A novel "five-in-one" comprehensive medical care framework for rehabilitation and nursing. | With the evolution of society, the world has entered a moderate stage of aging. Not surprisingly, the aging problem in the world is getting more intense, resulting in the increasing demand for higher-quality and well-organized medical and elderly care services. To cope with that, many researchers have dedicated themselves to advancing the medical care system based on data or platforms. However, they have ignored the life cycle, health service and management and the inevitable shift of living scenarios for the elderly. Therefore, the study aims to improve health conditions and enhance senior citizens' life quality and happiness index. In this paper, we build a unified body for people in their old age, bridging the disconnection between medical care and elderly care and constructing the prehensive medical care framework. It should be mentioned that the system takes the human life cycle as its axis, relies on the supply side and supply chain management, integrates medicine, industry, literature and science as methods, and takes health service management as a requirement. Furthermore, a case study on upper limb rehabilitation is elaborated along the prehensive medical care framework to confirm the effectiveness of the novel system. |
36896534 | Evolutionary game dynamics of cooperation in prisoner's dilemma with time delay. | Cooperation is an indispensable behavior in biological systems. In the prisoner's dilemma, due to the individual's selfish psychology, the defector is in the dominant position finally, which results in a social dilemma. In this paper, we discuss the replicator dynamics of the prisoner's dilemma with penalty and mutation. We first discuss the equilibria and stability of the prisoner's dilemma with a penalty. Then, the critical delay of the bifurcation with the payoff delay as the bifurcation parameter is obtained. In addition, considering the case of player mutation based on penalty, we analyze the two-delay system containing payoff delay and mutation delay and find the critical delay of Hopf bifurcation. Theoretical analysis and numerical simulations show that cooperative and defective strategies coexist when only a penalty is added. The larger the penalty is, the more players tend to cooperate, and the critical time delay of the time-delay system decreases with the increase in penalty. The addition of mutation has little effect on the strategy chosen by players. The two-time delay also causes oscillation. |
36896535 | Research on the heterogeneous effects of residents' income on mental health. | The influence of residents' e on mental health plex, and there are heterogeneous effects of residents' e on different types of mental health. Based on the annual panel data of 55 countries from 2007 to 2019, this paper divides residents' e into three dimensions: absolute e, relative e and e gap. Mental health is divided into three aspects: subjective well-being, prevalence of depression and prevalence of anxiety. Panel Tobit model is used to study the heterogeneous impact of residents' e on mental health. The results show that, on the one hand, different dimensions of residents' e have a heterogeneous impact on mental health, specifically, absolute e has a positive impact on mental health, while relative e and e gap have no significant impact on mental health. On the other hand, the impact of different dimensions of residents' e on different types of mental health is heterogeneous. Specifically, absolute e and e gap have heterogeneous effects on different types of mental health, while relative e has no significant impact on different types of mental health. |
36896536 | On the stability of the diffusive and non-diffusive predator-prey system with consuming resources and disease in prey species. | This research deals with formulating a multi-species eco-epidemiological mathematical model when the interacting pete for the same food sources and the prey species have some infection. It is assumed that infection does not spread vertically. Infectious diseases severely affect the population dynamics of prey and predator. One of the most important factors in population dynamics is the movement of species in the habitat in search of resources or protection. The ecological influences of diffusion on the population density of both species are studied. The study also deals with the analysis of the effects of diffusion on the fixed points of the proposed model. The fixed points of the model are sorted out. The Lyapunov function is constructed for the proposed model. The fixed points of the proposed model are analyzed through the use of the Lyapunov stability criterion. It is proved that coexisting fixed points remain stable under the effects of self-diffusion, whereas, in the case of cross-diffusion, Turing instability exists conditionally. Moreover, a two-stage explicit numerical scheme is constructed, and the stability of the said scheme is found by using von Neumann stability analysis. Simulations are performed by using the constructed scheme to discuss the model's phase portraits and time-series solution. Many scenarios are discussed to display the present study's significance. The impacts of the transmission parameter 𝛾 and food resource f on the population density of species are presented in plots. It is verified that the availability mon food resources greatly influences the dynamics of such models. It is shown that all three classes, i.e., the predator, susceptible prey and infected prey, can coexist in the habitat, and this coexistence has a stable nature. Hence, in the realistic scenarios of predator-prey ecology, the results of the study show the importance of food availability for the interacting species. |
36896537 | Dirichlet problems of fractional p-Laplacian equation with impulsive effects. | The purpose of the article is to investigate Dirichlet boundary-value problems of the fractional p-Laplacian equation with impulsive effects. By using the Nehari manifold method, mountain pass theorem and three critical points theorem, some new results are achieved under more general growth conditions. In addition, this paper weakens monly used p-suplinear and p-sublinear growth conditions. |
36896538 | Optimal modeling of anti-breast cancer candidate drugs screening based on multi-model ensemble learning with imbalanced data. | The imbalanced data makes the machine learning model seriously biased, which leads to false positive in screening of therapeutic drugs for breast cancer. In order to deal with this problem, a multi-model ensemble framework based on tree-model, linear model and deep-learning model is proposed. Based on the methodology constructed in this study, we screened the 20 most critical molecular descriptors from 729 molecular descriptors of 1974 anti-breast cancer drug candidates and, in order to measure the pharmacokinetic properties and safety of the drug candidates, the screened molecular descriptors were used in this study for subsequent bioactivity, absorption, distribution metabolism, excretion, toxicity, and other prediction tasks. The results show that the method constructed in this study is superior and more stable than the individual models used in the ensemble approach. |
36896539 | Safety action over oscillations of a beam excited by moving load via a new active vibration controller. | This paper presents a mixed active controller (NNPDCVF) bines cubic velocity feedback with a negative nonlinear proportional derivative to reduce the nonlinear vibrating behavior of a nonlinear dynamic beam system. Multiple time-scales method treatment is produced to get the mathematical solution of the equations for the dynamical modeling with NNPDCVF controller. This research focuses on two resonance cases which are the primary and 1/2 subharmonic resonances. Time histories of the primary system and the controller are shown to demonstrate the reaction with and without control. The time-history response, as well as the impacts of the parameters on the system and controller, are simulated numerically using the MATLAB program. Routh-Hurwitz criterion is used to examine the stability of the system under primary resonance. A numerical simulation, using the MATLAB program software, is obtained to show the time-history response, the effect of the parameters on the system and the controller. An investigation is done into how different significant effective coefficients affect the resonance's steady-state response. The results demonstrate that the main resonance response is occasionally impacted by the new active feedback control's ability to effectively attenuate amplitude. Choosing an appropriate control Gaining quantity can enhance the effectiveness of vibration control by avoiding the primary resonance zone and unstable multi-solutions. Optimum control parameter values are calculated. Validation curves are provided to show how closely the perturbation and numerical solutions are related. |
36896540 | An optimal control problem without control costs. | A two-dimensional diffusion process is controlled until it enters a given subset of $ \mathbb{R}^2 $. The aim is to find the control that minimizes the expected value of a cost function in which there are no control costs. The optimal control can be expressed in terms of the value function, which gives the smallest value that the expected cost can take. To obtain the value function, one can make use of dynamic programming to find the differential equation it satisfies. This differential equation is a non-linear second-order partial differential equation. We find explicit solutions to this non-linear equation, subject to the appropriate boundary conditions, in important particular cases. The method of similarity solutions is used. |
36896541 | Extremal values of VDB topological indices over F-benzenoids with equal number of edges. | The utilization of molecular structure topological indices is currently a standing operating procedure in the structure-property relations research, especially in QSPR/QSAR study. In the past several year, generous molecular topological indices related to some chemical and physical properties of pounds were put forward. Among these topological indices, the VDB topological indices rely only on the vertex degree of chemical molecular graphs. The VDB topological index of an $ n $-order graph $ G $ is defined as TI(G) = \sum\limits_{1\leq i\leq j\leq n-1}m_{ij}\psi_{ij}, $ where $ \{\psi_{ij}\} $ is a set of real numbers, $ m_{ij} $ is the quantity of edges linking an $ i $-vertex and another $ j $-vertex. Numerous famous topological indices are special circumstance of this expression. f-benzenoids are a kind of polycyclic aromatic hydrocarbons, present in large amounts in coal tar. Studying the properties of f-benzenoids via topological indices is a worthy task. In this work the extremum $ TI $ of f-benzenoids with given number of edges were determined. The main idea is to construct f-benzenoids with maximal number of inlets and simultaneously minimal number of hexagons in $ \Gamma_{m} $, where $ \Gamma_{m} $ is the collection of f-benzenoids with exactly $ m $ $ (m\geq19) $ edges. As an application of this result, we give a unified approach of VDB topological indices to predict distinct chemical and physical properties such as the boiling point, $ \pi $-electrom energy, molecular weight and vapour pressure etc. of f-benzenoids with fixed number of edges. |
36896542 | Recent advancements in digital health management using multi-modal signal monitoring. | Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability municate data over a network without requiring human-to-human or puter interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery. |
36896543 | A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data. | As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively. |
36896544 | Blow-up and boundedness in quasilinear attraction-repulsion systems with nonlinear signal production. | In this paper, we consider the quasilinear parabolic-elliptic-elliptic attraction-repulsion system $ \begin{equation} \nonumber \left\{ \begin{split} &u_t = \nabla\cdot(D(u)\nabla u)-\chi\nabla\cdot(u\nabla v)+\xi\nabla\cdot(u\nabla w),&\qquad &x\in\Omega,\,t>0, \\ & 0 = \Delta v-\mu_{1}(t)+f_{1}(u),&\qquad &x\in\Omega,\,t>0, \\ &0 = \Delta w-\mu_{2}(t)+f_{2}(u),&\qquad &x\in\Omega,\,t>0 \end{split} \right. \end{equation} $ under homogeneous Neumann boundary conditions in a smooth bounded domain $ \Omega\subset\mathbb{R}^n, \ n\geq2 $. The nonlinear diffusivity $ D $ and nonlinear signal productions $ f_{1}, f_{2} $ are supposed to extend the prototypes $ \begin{equation} \nonumber D(s) = (1+s)^{m-1},\ f_{1}(s) = (1+s)^{\gamma_{1}},\ f_{2}(s) = (1+s)^{\gamma_{2}},\ s\geq0,\gamma_{1},\gamma_{2}>0,m\in\mathbb{R}. \end{equation} $ We proved that if $ \gamma_{1} > \gamma_{2} $ and $ 1+\gamma_{1}-m > \frac{2}{n} $, then the solution with initial mass concentrating enough in a small ball centered at origin will blow up in finite time. However, the system admits a global bounded classical solution for suitable smooth initial datum when $ \gamma_{2} < 1+\gamma_{1} < \frac{2}{n}+m $. |
36896545 | Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text. | Though several AI-based models have been established for COVID-19 diagnosis, the machine-based diagnostic gap is still ongoing, making further efforts bat this epidemic imperative. So, we tried to create a new feature selection (FS) method because of the persistent need for a reliable system to choose features and to develop a model to predict the COVID-19 virus from clinical texts. This study employs a newly developed methodology inspired by the flamingo's behavior to find a near-ideal feature subset for accurate diagnosis of COVID-19 patients. The best features are selected using a two-stage. In the first stage, we implemented a term weighting technique, which that is RTF-C-IEF, to quantify the significance of the features extracted. The second stage involves using a newly developed feature selection approach called the improved binary flamingo search algorithm (IBFSA), which chooses the most important and relevant features for COVID-19 patients. The proposed multi-strategy improvement process is at the heart of this study to improve the search algorithm. The primary objective is to broaden the algorithm's capabilities by increasing diversity and support exploring the algorithm search space. Additionally, a binary mechanism was used to improve the performance of traditional FSA to make it appropriate for binary FS issues. Two datasets, totaling 3053 and 1446 cases, were used to evaluate the suggested model based on the Support Vector Machine (SVM) and other classifiers. The results showed that IBFSA has the best pared to numerous previous swarm algorithms. It was noted, that the number of feature subsets that were chosen was also drastically reduced by 88% and obtained the best global optimal features. |
36896546 | Global investigation for an "SIS" model for COVID-19 epidemic with asymptomatic infection. | In this paper, we analyse a dynamical system taking into account the asymptomatic infection and we consider optimal control strategies based on a regular network. We obtain basic mathematical results for the model without control. pute the basic reproduction number (R) by using the method of the next generation matrix then we analyse the local stability and global stability of the equilibria (disease-free equilibrium (DFE) and endemic equilibrium (EE)). We prove that DFE is LAS (locally asymptotically stable) when R<1 and it is unstable when R>1. Further, the existence, the uniqueness and the stability of EE is carried out. We deduce that when R>1, EE exists and is unique and it is LAS. By using generalized Bendixson-Dulac theorem, we prove that DFE is GAS (globally asymptotically stable) if R<1 and that the unique endemic equilibrium is globally asymptotically stable when R>1. Later, by using Pontryagin's maximum principle, we propose several reasonable optimal control strategies to the control and the prevention of the disease. We mathematically formulate these strategies. The unique optimal solution was expressed using adjoint variables. A particular numerical scheme was applied to solve the control problem. Finally, several numerical simulations that validate the obtained results were presented. |
36896547 | Improved prognostic prediction model for liver cancer based on biomarker data screened by combined methods. | Liver cancer is mon cause of death from cancer in the population, with the 4th highest mortality rate from cancer worldwide. The high recurrence rate of hepatocellular carcinoma after surgery is an important cause of high mortality among patients. In this paper, based on eight scheduled core markers of liver cancer, an improved feature screening algorithm was proposed based on the analysis of the basic principles of the random forest algorithm, and the system was finally applied to liver cancer prognosis prediction to improve the prediction of biomarkers for liver cancer recurrence, and the impact of different algorithmic strategies on the prediction accuracy pared and analyzed. The results showed that the improved feature screening algorithm was able to reduce the feature set by about 50% while ensuring that the prediction accuracy was reduced within 2%. |
36896548 | Research of mortality risk prediction based on hospital admission data for COVID-19 patients. | As COVID-19 continues to spread across the world and causes hundreds of millions of infections and millions of deaths, medical institutions around the world keep facing a crisis of medical runs and shortages of medical resources. In order to study how to effectively predict whether there are risks of death in patients, a variety of machine learning models have been used to learn and predict the clinical demographics and physiological indicators of COVID-19 patients in the United States of America. The results show that the random forest model has the best performance in predicting the risk of death in hospitalized patients with COVID-19, as the COVID-19 patients' mean arterial pressures, ages, C-reactive protein tests' values, values of blood urea nitrogen and their clinical troponin values are the most important implications for their risk of death. Healthcare organizations can use the random forest model to predict the risks of death based on data from patients admitted to a hospital due to COVID-19, or to stratify patients admitted to a hospital due to COVID-19 based on the five key factors this can optimize the diagnosis and treatment process by appropriately arranging ventilators, the intensive care unit and doctors, thus promoting the efficient use of limited medical resources during the COVID-19 pandemic. Healthcare organizations can also establish databases of patient physiological indicators and use similar strategies to deal with other pandemics that may occur in the future, as well as save more lives threatened by infectious diseases. Governments and people also need to take action to prevent possible future pandemics. |
36896549 | Identification of influential observations in high-dimensional survival data through robust penalized Cox regression based on trimming. | Penalized Cox regression can efficiently be used for the determination of biomarkers in high-dimensional genomic data related to disease prognosis. However, results of Penalized Cox regression is influenced by the heterogeneity of the samples who have different dependent structure between survival time and covariates from most individuals. These observations are called influential observations or outliers. A robust penalized Cox model (Reweighted Elastic Net-type maximum trimmed partial likelihood estimator, Rwt MTPL-EN) is proposed to improve the prediction accuracy and identify influential observations. A new algorithm AR-Cstep to solve Rwt MTPL-EN model is also proposed. This method has been validated by simulation study and application to glioma microarray expression data. When there were no outliers, the results of Rwt MTPL-EN were close to the Elastic Net (EN). When outliers existed, the results of EN were impacted by outliers. And whenever the censored rate was large or low, the robust Rwt MTPL-EN performed better than EN. and could resist the outliers in both predictors and response. In terms of outliers detection accuracy, Rwt MTPL-EN was much higher than EN. The outliers who "lived too long" made EN perform worse, but were accurately detected by Rwt MTPL-EN. Through the analysis of glioma gene expression data, most of the outliers identified by EN were those "failed too early", but most of them were not obvious outliers according to risk estimated from omics data or clinical variables. Most of the outliers identified by Rwt MTPL-EN were those who "lived too long", and most of them were obvious outliers according to risk estimated from omics data or clinical variables. Rwt MTPL-EN can be adopted to detect influential observations in high-dimensional survival data. |
36896550 | A generalized distributed delay model of COVID-19: An endemic model with immunity waning. | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R_c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19. |
36896551 | Integrated whole transcriptome analysis for the crucial regulators and functional pathways related to cardiac fibrosis in rats. | Cardiac fibrosis has gradually gained significance in the field of cardiovascular disease; however, its specific pathogenesis remains unclear. This study aims to establish the regulatory networks based on whole-transcriptome RNA sequencing analyses and reveal the underlying mechanisms of cardiac fibrosis. |
36896552 | Bioinformatic analysis of the coding region of the melatonin receptor 1b gene as a reliable DNA marker to resolve interspecific mammal phylogenetic relationships. | This research looks into the main DNA markers and the limits of their application in molecular phylogenetic analysis. Melatonin 1B (MTNR1B) receptor genes were analyzed from various biological sources. Based on the coding sequences of this gene, using the class Mammalia as example, phylogenetic reconstructions were made to study the potential of mtnr1b as a DNA marker for phylogenetic relationships investigating. The phylogenetic trees were constructed using NJ, ME and ML methods that establish the evolutionary relationships between different groups of mammals. The resulting topologies were generally in good agreement with topologies established on the basis of morphological and archaeological data as well as with other molecular markers. The present divergences provided a unique opportunity for evolutionary analysis. These results suggest that the coding sequence of the MTNR1B gene can be used as a marker to study the relationships of lower evolutionary levels (order, species) as well as to resolve deeper branches of the phylogenetic tree at the infraclass level. |
36896553 | Investigation of the evolution of tumor-induced microvascular network under the inhibitory effect of anti-angiogenic factor, angiostatin: A mathematical study. | Anti-angiogenesis as a treatment strategy for normalizing the microvascular network of tumors is of great interest among researchers, especially bination with chemotherapy or radiotherapy. According to the vital role that angiogenesis plays in tumor growth and in exposing the tumor to therapeutic agents, this work develops a mathematical framework to study the influence of angiostatin, a plasminogen fragment that shows the anti-angiogenic function, in the evolutionary behavior of tumor-induced angiogenesis. Angiostatin-induced microvascular network reformation is investigated in a two-dimensional space by considering two parent vessels around a circular tumor by a modified discrete angiogenesis model in different tumor sizes. The effects of imposing modifications on the existing model, i.e., the matrix-degrading enzyme effect, proliferation and death of endothelial cells, matrix density function, and a more realistic chemotactic function, are investigated in this study. Results show a decrease in microvascular density in response to the angiostatin. A functional relationship exists between angiostatin's ability to normalize the capillary network and tumor size or progression stage, such that capillary density decreases by 55%, 41%, 24%, and 13% in tumors with a non-dimensional radius of 0.4, 0.3, 0.2, and 0.1, respectively, after angiostatin administration. |
36896554 | MRE: A translational knowledge graph completion model based on multiple relation embedding. | Knowledge pletion (KGC) has attracted significant research interest in applying knowledge graphs (KGs). Previously, many works have been proposed to solve the KGC problem, such as a series of translational and semantic matching models. However, most previous methods suffer from two limitations. First, current models only consider the single form of relations, thus failing to simultaneously capture the semantics of multiple relations (direct, multi-hop and rule-based). Second, the data-sparse problem of knowledge graphs would make part of relations challenging to embed. This paper proposes a novel translational knowledge pletion model named multiple relation embedding (MRE) to address the above limitations. We attempt to embed multiple relations to provide more semantic information for representing KGs. To be more specific, we first leverage PTransE and AMIE+ to extract multi-hop and rule-based relations. Then, we propose two specific encoders to encode extracted relations and capture semantic information of multiple relations. We note that our proposed encoders can achieve interactions between relations and connected entities in relation encoding, which is rarely considered in existing methods. Next, we define three energy functions to model KGs based on the translational assumption. At last, a joint training method is adopted to perform KGC. Experimental results illustrate that MRE outperforms other baselines on KGC, demonstrating the effectiveness of embedding multiple relations for advancing knowledge pletion. |
36896555 | A multimedia knowledge discovery-based optimal scheduling approach considering visual behavior in smart education. | Nowadays, the convergence of puting technique and education has been a hot concern for both academia and industry, producing the conception of smart education. It is predictable that automatic planning and scheduling for course contents are the most practical important task for smart education. As online and offline educational activities are visual behaviors, it remains challenging to capture and extract principal features. To breakthrough current barriers, this bines the visual perception technology and data mining theory, and proposes a multimedia knowledge discovery-based optimal scheduling approach in smart education about painting. At first, the data visualization is carried out to analyze the adaptive design of visual morphologies. On this basis, it is supposed to formulate a multimedia knowledge discovery framework which can implement multimodal inference tasks, so as to calculate specific course contents for specific individuals. At last, some simulation works are also conducted to obtain analysis results, showing that the proposed optimal scheduling scheme can work well in contents planning of smart education scenarios. |
36896557 | Adaptive predefined-time prescribed performance control for spacecraft systems. | The high-accuracy attitude maneuvering problem for spacecraft systems is investigated. A prescribed performance function and a shifting function are first employed to ensure the predefined-time stability of attitude errors and eliminate the constraints on tracking errors at the incipient stage. Subsequently, a novel predefined-time control scheme is developed bining prescribed performance control and backstepping control procedures. Radial basis function neural network and minimum learning parameter techniques are introduced to model the function of lumped uncertainty including inertial uncertainties, actuator faults and virtual control law derivatives. According to the rigorous stability analysis, the preset tracking precision can be achieved within a predefined time and the fixed-time boundedness of all closed-loop signals is established. Finally, the efficacy of the propounded control scheme is manifested through numerical simulation results. |
36896558 | Spatial distribution of floating population in Beijing, Tianjin and Hebei Region and its correlations with synergistic development. | Utilizing statistical information from the Seventh National Population Census, statistical yearbook and sampling dynamic survey data, this study examines the distribution characteristics of the floating population in Beijing, Tianjin and Hebei Region as well as the growth trend of the floating population in each region. It also makes assessments using floating population concentration and The Moran Index Computing Methods. According to the study, the spatial distribution of the floating population has a clear clustering pattern in Beijing, Tianjin and Hebei region. Beijing, Tianjin and Hebei region's mobile population growth patterns differ substantially, and the region's inflow population is mostly made up of migrant inhabitants of domestic provinces and inflow of people from nearby regions. Most of the mobile population resides in Beijing and Tianjin, whereas the outflow of people originates in Hebei province. The diffusion impact and the spatial features of the floating population in the Beijing, Tianjin and Hebei area have a constant, positive association, according to the timeline between 2014 and 2020. |
36896559 | The modeling and analysis of the COVID-19 pandemic with vaccination and isolation: a case study of Italy. | The global spread of COVID-19 has not been effectively controlled. It poses a significant threat to public health and global economic development. This paper uses a mathematical model with vaccination and isolation treatment to study the transmission dynamics of COVID-19. In this paper, some basic properties of the model are analyzed. The control reproduction number of the model is calculated and the stability of the disease-free and endemic equilibria is analyzed. The parameters of the model are obtained by fitting the number of cases that were detected as positive for the virus, dead, and recovered between January 20 and June 20, 2021, in Italy. We found that vaccination better controlled the number of symptomatic infections. A sensitivity analysis of the control reproduction number has been performed. Numerical simulations demonstrate that reducing the contact rate of the population and increasing the isolation rate of the population are effective non-pharmaceutical control measures. We found that if the isolation rate of the population is reduced, a short-term decrease in the number of isolated individuals can lead to the disease not being controlled at a later stage. The analysis and simulations in this paper may provide some helpful suggestions for preventing and controlling COVID-19. |
36896560 | Service scheduling optimization for multiple tower cranes considering the interval time of the cross-tasks. | The key issues that have always affected the production yield of the construction industry are delays and cost overruns, especially when dealing with large-scale projects and super-high buildings in which multiple tower cranes with overlapping areas are often deployed because of urgent due date and limited space. The service scheduling of tower cranes, which act as the crucial site equipment for lifting and transporting materials, is one of the main problems not only related to the construction progress and project cost but also affecting equipment health, and it may bring security risks. The current work presents a multi-objective optimization model for a multiple tower cranes service scheduling problem (MCSSP) with overlapping areas while achieving maximum interval time of cross-tasks and minimum makespan. For the solving procedure, NSGA-Ⅱ is employed with double-layer chromosome coding and simultaneous coevolutionary strategy design, which can obtain a satisfactory solution through effectively allocating tasks within overlapping areas to each crane and then prioritizing all the assigned tasks. The makespan was minimized, and stable operation of tower cranes without collision was achieved by maximizing the cross-tasks interval time. A case study of the megaproject Daxing International Airport in China has been conducted to evaluate the proposed model and algorithm. putational results illustrated the Pareto front and its non-dominant relationship. The Pareto optimal solution outperforms the results of the single objective classical genetic algorithm in terms of overall performance of makespan and interval time of cross-tasks. It also can be seen that significant improvement in the time interval of cross-tasks can be achieved at the cost of a tiny increase in makespan, which means effective avoidance of the tower cranes entering the overlapping area at the same time. This can help eliminate collision, interference and frequent start-up and braking of tower cranes, leading to safe, stable and efficient operation on the construction site. |
36896561 | Research and implementation of variable-domain fuzzy PID intelligent control method based on Q-Learning for self-driving in complex scenarios. | In the control of the self-driving vehicles, PID controllers are widely used due to their simple structure and good stability. However, plex self-driving scenarios such as curvature curves, car following, overtaking, etc., it is necessary to ensure the stable control accuracy of the vehicles. Some researchers used fuzzy PID to dynamically change the parameters of PID to ensure that the vehicle control remains in a stable state. It is difficult to ensure the control effect of the fuzzy controller when the size of the domain is not selected properly. This paper designs a variable-domain fuzzy PID intelligent control method based on Q-Learning to make the system robust and adaptable, which is dynamically changed the size of the domain to further ensure the control effect of the vehicle. The variable-domain fuzzy PID algorithm based on Q-Learning takes the error and the error rate of change as input and uses the Q-Learning method to learn the scaling factor online so as to achieve online PID parameters adjustment. The proposed method is verified on the Panosim simulation platform.The experiment shows that the accuracy is improved by pared with the traditional fuzzy PID, which reflects the effectiveness of the algorithm. |
36896562 | Selective Construction of Molecular Borromean Rings and [2]Catenane Utilizing Ether Bipyridyl Ligands. | A series of Cp |
36896564 | Use of Ag-Au-ICG to increase fluorescence image of human hepatocellular carcinoma cell lines. | Indocyanine green (ICG) is effective for a variety of applications including liver tumour imaging and operates in the near-infrared window. Agents for near-infrared imaging are, however, still in clinical development. The present study aimed to prepare and investigate fluorescence emission properties of ICG bination with Ag-Au in order to enhance their specific interactions with human hepatocellular carcinoma cell lines (HepG-2). The plex was prepared |
36896565 | A prospective observational evaluation of an online health care professional training program to promote healthy pregnancy weight gain. | A lack of programs to develop clinician knowledge and confidence to address weight gain within pregnancy is a barrier to the provision of evidence-based care. |
36896566 | A single-center retrospective review of pediatric cases of progressive transformation of germinal centers. | Progressive transformation of germinal centers (PTGC) is a rare diagnosis characterized by asymptomatic lymph node enlargement. It has previously been associated with lymphoma, autoimmune conditions, and lymphoproliferative diseases in small pediatric case series. |
36896567 | circDENND4C, a novel serum marker for epithelial ovarian cancer, acts as a tumor suppressor by downregulating miR-200b/c. | To explore the diagnostic value of circ-DENN domain containing 4 C (circDENND4C) in epithelial ovarian cancer (EOC) and the corresponding mechanism. |
36896568 | Increasing diversity, equity, and inclusion in the fields of nutrition and obesity: A road map to equity in academia. | Research shows that a diverse faculty improves academic, clinical, and research es in higher education. Despite that, persons in minority groups, usually categorized by race or ethnicity, are underrepresented in academia (URiA). The Nutrition Obesity Research Centers (NORCs), supported by the National Institute of Diabetes and Digestive and Kidney Diseases, hosted workshops on five separate days in September and October 2020. NORCs convened these workshops to identify barriers and facilitators for diversity, equity, and inclusion (DEI) and provide specific mendations to improve DEI within obesity and nutrition for individuals from URiA groups. Recognized experts on DEI presented each day, after which the NORCs conducted breakout sessions with key stakeholders who engage in nutrition and obesity research. The breakout session groups included early-career investigators, professional societies, and academic leadership. The consensus from the breakout sessions was that glaring inequities affect URiA in nutrition and obesity, particularly related to recruitment, retention, and advancement. mendations from the breakout sessions to improve DEI across academia focused on six themes: (1) recruitment, (2) retention, (3) advancement, (4) intersectionality of multiple challenges (e.g., being Black and a woman), (5) funding agencies, and (6) implementation of strategies to address problems related to DEI. |
36896569 | Improving Home Caregiver Independence With Central Line Care for Pediatric Cancer Patients. | Home caregivers (eg parents) of pediatric patients with cancer with external central lines (CL) must carefully maintain this device to plications. No guidelines exist to support caregiver skill development, assess petency, follow-up after initial CL teaching, and support progress over time. We aimed to achieve >90% caregiver independence with CL care within 1 year through a family-centered quality improvement intervention. |
36896570 | Alpha-1 antitrypsin deficiency: current therapy and emerging targets. | Alpha1 antitrypsin deficiency (AATD), mon hereditary disorder affecting mainly lungs, liver and skin has been the focus of some of the most exciting therapeutic approaches in medicine in the past 5 years. In this review, we discuss the therapies presently available for the different manifestations of AATD and new therapies in the pipeline. |
36896571 | Clinical, cytological and ultrasonographic features of incidental thyroid cancer in a hospital-based study in vietnam. | Thyroid nodules mon diseases of the endocrine system, with a 5% prevalence rate in the general population. This study aimed to identify prevalence, clinical, cytological and ultrasonographic features of incidental thyroid cancer and its associated factors in Vietnam. |
36896572 | Incidence and Mortality of Children Receiving Home Mechanical Ventilation. | The incidence, as well as the predictors of mortality, for children receiving home mechanical ventilation (HMV) using population-based data in Canada is a current knowledge gap. Our objectives were to describe HMV incidence and mortality rates, and associations of demographic and clinical variables on mortality. |
36896573 | Evaluation of blood cellular and biochemical parameters in rats under a chronic hypoxic environment at high altitude. | The purpose of this study was to explore the changes in blood cellular and biochemical parameters of rats in a natural environment of low pressure and low oxygen on the plateau. |
36896575 | The human sperm proteome-Toward a panel for male fertility testing. | Although male factor accounts for 40%-50% of unintended childlessness, we are far from fully understanding the detailed causes. Usually, affected men cannot even be provided with a molecular diagnosis. |
36896577 | Spray-Flame Synthesis of LaFe | The product properties of mixed oxide nanoparticles generated via spray-flame synthesis depend on an intricate interplay of solvent and precursor chemistries in the processed solution. The effect of two different sets of metal precursors, acetates and nitrates, dissolved in a mixture of ethanol (35 Vol.%) and 2-ethylhexanoic acid (2-EHA, 65 Vol.%) was investigated for the synthesis of LaFe |
36896578 | First report of outcomes in patients with stage IIIb AL amyloidosis treated with Dara-VCD front-line therapy. | Although Dara-VCD (daratumumab-bortezomib-cyclophosphamide-dexamethasone) has revolutionized the treatment of newly diagnosed Amyloid Light chain (AL) amyloidosis, patients with stage IIIb disease were excluded in the pivotal trial. We performed a multicentre retrospective cohort study to investigate the es of 19 consecutive patients treated with Dara-VCD front-line therapy who had stage IIIb AL at diagnosis. More than two thirds presented with New York Heart Association Class III/IV symptoms, and had a median of two organs involved (range, 2-4). The haematologic overall response rate was 100%, with 17/19 patients (89.5%) achieving a very good partial response (VGPR) or better. Haematologic responses were achieved rapidly, as evidenced by 63% of evaluable patients with involved serum free light chains (iFLC) < 2 mg/dl and the difference between involved and uninvolved serum free light chains (dFLC) <1 mg/dl at three months. Among 18 evaluable patients, 10 (56%) achieved a cardiac organ response and six (33%) cardiac VGPR or better. The median time to first cardiac response was 1.9 months (range, 0.4-7.3). At a median follow-up of 12 months for surviving patients, estimated one-year overall survival was 67.5% [95% confidence interval (CI), 43.8-84.7]. The incidence of grade 3 or higher infections was 21%, with no infection-related mortality thus far. In summary, Dara-VCD has a promising efficacy and safety profile in stage IIIb AL, and should be studied in prospective trials. |
36896579 | Quadratic Spin-Orbit Mechanism of the Electronic g-Tensor. | Understanding how the electronic g-tensor is linked to the electronic structure is desirable for the correct interpretation of electron paramagnetic resonance spectra. For pounds with large spin-orbit (SO) effects, this is still pletely clear. We report our investigation of quadratic SO contributions to the g-shift in heavy transition plexes. We implemented third-order perturbation theory in order to analyze the contributions arising from frontier molecular spin orbitals (MSOs). We show that the dominant quadratic SO term─spin-Zeeman (SO |
36896580 | Meta-QTL s and haplotypes for efficient zinc biofortification of rice. | Biofortification of rice with improved grain zinc (Zn) content is the most sustainable and cost-effective approach to address Zn malnutrition in Asia. Genomics-assisted breeding using precise and consistent Zn quantitative trait loci (QTLs), genes, and haplotypes can fast-track the development of Zn biofortified rice varieties. We conducted the meta-analysis of 155 Zn QTLs reported from 26 different studies. Results revealed 57 meta-QTLs with a significant reduction of 63.2% and 80% in the number and confidence interval of the Zn QTLs, respectively. Meta-quantitative trait loci (MQTLs) regions were found to be enriched with diverse metal homeostasis genes; at least 11 MQTLs were colocated with 20 known major genes involved in the production of root exudates, metal uptake, transport, partitioning, and loading into grains in rice. These genes were differentially expressed in vegetative and reproductive tissues, and plex web of interactions were observed among them. We identified superior haplotypes and binations for nine candidate genes (CGs), and the frequency and allelic effects of superior haplotypes varied in different subgroups. The precise MQTLs with high phenotypic variance, CGs, and superior haplotypes identified in our study are useful for an efficient Zn biofortification of rice and to ensure Zn as an ponent of all the future rice varieties through mainstreaming of Zn breeding. |
36896581 | Drug-Loading Content Influences Cellular Uptake of Polymer-Coated Nanocellulose. | While the effects of nanoparticle properties such as shape and size on cellular uptake are widely studied, influences exerted by drug loading have so far been ignored. In this work, nanocellulose (NC) coated by Passerini reaction with poly(2-hydroxy ethyl acrylate) (PHEA- |
36896582 | Competitive aminal formation during the synthesis of a highly soluble, isopropyl-decorated imine porous organic cage. | The synthesis of a new porous organic cage decorated with isopropyl moieties (CC21) was achieved from the reaction of triformylbenzene and an isopropyl functionalised diamine. Unlike structurally analogous porous organic cages, its synthesis proved challenging due petitive aminal formation, rationalised using control experiments putational modelling. The use of an additional amine was found to increase conversion to the desired cage. |
36896583 | A Per-Protocol Analysis Using Inverse-Probability-of-Censoring Weights in a Randomized Trial of Initial Protease Inhibitor Versus Nonnucleoside Reverse Transcriptase Inhibitor Regimens in Children. | Protocol adherence may influence measured treatment effectiveness in randomized controlled trials. Using data from a multicenter trial (Europe and the Americas, 2002-2009) of children with human immunodeficiency virus type 1 who had been randomized to receive initial protease inhibitor (PI) versus nonnucleoside reverse transcriptase inhibitor (NNRTI) antiretroviral therapy regimens, we generated time-to-event intention-to-treat (ITT) estimates of treatment effectiveness, applied inverse-probability-of-censoring weights to generate per-protocol efficacy estimates, pared shifts from ITT to per-protocol estimates across and within treatment arms. In ITT analyses, 263 participants experienced 4-year treatment failure probabilities of 41.3% for PIs and 39.5% for NNRTIs (risk difference = 1.8% (95% confidence interval (CI): -10.1, 13.7); hazard ratio = 1.09 (95% CI: 0.74, 1.60)). In per-protocol analyses, failure probabilities were 35.6% for PIs and 29.2% for NNRTIs (risk difference = 6.4% (95% CI: -6.7, 19.4); hazard ratio = 1.30 (95% CI: 0.80, 2.12)). Within-arm shifts in failure probabilities from ITT to per-protocol analyses were 5.7% for PIs and 10.3% for NNRTIs. Protocol nonadherence was nondifferential across arms, suggesting that possibly better NNRTI efficacy may have been masked by differences in within-arm shifts deriving from differential regimen forgiveness, residual confounding, or chance. A per-protocol approach using inverse-probability-of-censoring weights facilitated evaluation of relationships among adherence, efficacy, and forgiveness applicable to pediatric oral antiretroviral regimens. |
36896585 | Testing Whether Higher Contact Among the Vaccinated Can Be a Mechanism for Observed Negative Vaccine Effectiveness. | Evidence from early observational studies suggested negative vaccine effectiveness (${V}_{Eff}$) for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant. Since true ${V}_{Eff}$ is unlikely to be negative, we explored how differences in contact among vaccinated persons (e.g., potentially from the implementation of vaccine mandates) could lead to observed negative ${V}_{Eff}$. Using a susceptible-exposed-infectious-recovered (SEIR) transmission model, we examined how vaccinated-contact heterogeneity, defined as an increase in the contact rate only between vaccinated individuals, interacted with 2 mechanisms of vaccine efficacy: vaccine efficacy against susceptibility ($V{E}_S$) and vaccine efficacy against infectiousness ($V{E}_I$), to produce underestimated and in some cases, negative measurements of ${V}_{Eff}$. We found that vaccinated-contact heterogeneity led to negative estimates when $V{E}_I$, and especially $V{E}_S$, were low. Moreover, we determined that when contact heterogeneity was very high, ${V}_{Eff}$ could still be underestimated given relatively high vaccine efficacies (0.7), although its effect on ${V}_{Eff}$ was strongly reduced. We also found that this contact heterogeneity mechanism generated a signature temporal pattern: The largest underestimates and negative measurements of ${V}_{Eff}$ occurred during epidemic growth. Overall, our research illustrates how vaccinated-contact heterogeneity could have feasibly produced negative measurements during the Omicron period and highlights its general ability to bias observational studies of ${V}_{Eff}$. |
36896586 | Adrenal Medullary Hyperplasia: A Systematic Review and Meta-analysis. | Adrenal medullary hyperplasia (AMH) is a rare, pletely described disorder of the adrenal medulla that is associated with catecholamine excess. |
36896587 | Invited Commentary: Concealed Carrying of Firearms, Public Policy, and Opportunities for Mitigating Harm. | In the last 30 years, 25 US states have relaxed laws regulating the concealed carrying of firearms (concealed-carry weapons (CCW) laws). These changes may have substantial impacts on violent crime. In a recent study, Doucette et al. (Am J Epidemiol. 2023;192(3):342-355) used a synthetic control approach to assess the effects of shifting from more restrictive "may/no-issue" CCW laws to less restrictive "shall-issue" CCW laws on homicides, aggravated assaults, and robberies involving a gun mitted by other means. The study adds to the evidence that more permissive CCW laws have probably increased rates of firearm assault in states adopting these laws. Importantly, this study is the first to identify that specific provisions of shall-issue CCW laws-including denying permits to persons with violent misdemeanor convictions, a history of dangerous behavior, or "questionable character" and live-fire training requirements-may help mitigate harms associated with shall-issue CCW laws. These findings are timely and salient given the recent Supreme Court ruling striking down a defining element of may-issue laws. This thorough study offers actionable results and provides a methodological model for state firearm policy evaluations. Its limitations reflect the needs of the field more broadly: greater focus on racial/ethnic equity and within-state variation, plus strengthening the data infrastructure on firearm violence and crime. |
36896590 | Genomics, Population Divergence, and Historical Demography of the World's Largest and Endangered Butterfly, The Queen Alexandra's Birdwing. | The world's largest butterfly is the microendemic Papua New Guinean Ornithoptera alexandrae. Despite years of conservation efforts to protect its habitat and breed this up-to-28-cm butterfly, this species still figures as endangered in the IUCN Red List and is only known from two allopatric populations occupying a total of only ∼140 km². Here we aim at assembling reference genomes for this species to investigate its genomic diversity, historical demography and determine whether the population is structured, which could provide guidance for conservation programs attempting to (inter)breed the two populations. Using bination of long and short DNA reads and RNA sequencing, we assembled six reference genomes of the tribe Troidini, with four annotated genomes of O. alexandrae and two genomes of related species Ornithoptera priamus and Troides oblongomaculatus. We estimated the genomic diversity of the three species, and we proposed scenarios for the historical population demography using two polymorphism-based methods taking into account the characteristics of low-polymorphic invertebrates. Indeed, chromosome-scale assemblies reveal very low levels of nuclear heterozygosity across Troidini, which appears to be exceptionally low for O. alexandrae (lower than 0.01%). Demographic analyses demonstrate low and steadily declining Ne throughout O. alexandrae history, with a divergence into two distinct populations about 10,000 years ago. These results suggest that O. alexandrae distribution has been microendemic for a long time. It should also make local conservation programs aware of the genomic divergence of the two populations, which should not be ignored if any attempt is made to cross the two populations. |
36896589 | De novo Assembly and Comparative Analyses of Mitochondrial Genomes in Piperales. | The mitochondrial genome of Liriodendron tulipifera exhibits many ancestral angiosperm features and a remarkably slow evolutionary rate, while mitochondrial genomes of other magnoliids remain yet to be characterized. We assembled nine new mitochondrial genomes, representing all genera of perianth-bearing Piperales, as well as for a member of the sister clade: plete or plete mitochondrial genomes from Aristolochiaceae and six additional draft assemblies including Thottea, Asaraceae, Lactoridaceae, and Hydnoraceae. parative purpose, plete mitochondrial genome was assembled for Saururus, a member of the perianth-less Piperales. The average number of short repeats (50-99 bp) was much larger in genus Aristolochia than in other angiosperm mitochondrial genomes, and approximately 30% of repeats (<350 bp) were found to have the capacity to mediate bination. We found mitochondrial genomes in perianth-bearing prising conserved repertories of protein-coding genes and rRNAs but variable copy numbers of tRNA genes. We identified several shifts from cis- to trans-splicing of the Group II introns of nad1i728, cox2i373, and nad7i209. Two short regions of the cox1 and atp8 genes were likely derived from independent horizontal gene transfer events in perianth-bearing Piperales. We found biased enrichment of specific substitution types in different lineages of magnoliids and the Aristolochiaceae family showed the highest ratio of A:T > T:A substitutions of all other investigated angiosperm groups. Our study reports the first mitochondrial genomes for Piperales and uses this new information for a better understanding of the evolutionary patterns of magnoliids and angiosperms in general. |
36896592 | Focus on Liver Function Abnormalities in Patients With Turner Syndrome: Risk Factors and Evaluation of Fibrosis Risk. | Liver function abnormalities (LFAs) have been described in patients with Turner syndrome (TS). Although a high risk of cirrhosis has been reported, there is a need to assess the severity of liver damage in a large cohort of adult patients with TS. |
36896594 | Nerve regeneration by interferon intervention in aging brain. | Neural stem cells (NSCs) are shielded from viral infection by interferon (IFN) defense. As individuals age, activation of NSC decreases with a significant decline of stemness marker Sex-determining region Y box 2 (Sox2) while IFN signaling enhances (Kalamakis et al, 2019). Given that low-level type-I IFN under normal physiological conditions can promote dormant hematopoietic stem cell differentiation (Baldridge et al, 2010), whether there is an inner connection between IFN signaling and NSC function remains elusive. In this issue of EMBO Molecular Medicine, Carvajal Ibanez et al (2023) reveal that IFN-β, a type-I interferon, induces cell-type-specific interferon-stimulated genes (ISGs) and regulates global protein synthesis by orchestrating mTOR1 activity and stem cell cycle that retain NSCs at the G |
36896595 | Reduction in hippocampal GABAergic transmission in a low birth weight rat model of depression. | Prenatal stress is believed to increase the risk of developing neuropsychiatric disorders, including major depression. Adverse genetic and environmental impacts during early development, such as glucocorticoid hyper-exposure, can lead to changes in the foetal brain, linked to mental illnesses developed in later life. Dysfunction in the GABAergic inhibitory system is associated with depressive disorders. However, the pathophysiology of GABAergic signalling is poorly understood in mood disorders. Here, we investigated GABAergic neurotransmission in the low birth weight (LBW) rat model of depression. Pregnant rats, exposed to dexamethasone, a synthetic glucocorticoid, during the last week of gestation, yielded LBW offspring showing anxiety- and depressive-like behaviour in adulthood. Patch-clamp recordings from dentate gyrus granule cells in brain slices were used to examine phasic and tonic GABA |
36896596 | Epigenetic Activation of Tensin 4 Promotes Gastric Cancer Progression. | Gastric cancer (GC) is plex disease influenced by multiple genetic and epigenetic factors. Chronic inflammation caused by |
36896597 | 7α,25-Dihydroxycholesterol-Induced Oxiapoptophagic Chondrocyte Death via the Modulation of p53-Akt-mTOR Axis in Osteoarthritis Pathogenesis. | This study aimed to exploring the pathophysiological mechanism of 7α,25-dihydroxycholesterol (7α,25-DHC) in osteoarthritis (OA) pathogenesis. 7α,25-DHC accelerated the proteoglycan loss in |
36896598 | The myxozoan parasite | Nile × blue tilapia hybrid ( |
36896599 | Impact of skeletal muscle mass evaluating methods on severity of metabolic associated fatty liver disease in non-elderly adults. | The study aimed to explore the relationships of skeletal muscle mass with disease severity in metabolic-associated fatty liver disease (MAFLD) patients with different methods. Consecutive subjects undergoing bioelectrical impedance analysis were included. The steatosis grade and liver fibrosis were evaluated by MRI-derived proton density fat fraction and two-dimensional shear wave elastography. The appendicular skeletal muscle mass (ASM) was adjusted by height |
36896601 | An otolaryngological tour of Vesalius' | Andreas Vesalius published his famous anatomy book, |
36896602 | Macrophage DCLK1 promotes atherosclerosis via binding to IKKβ and inducing inflammatory responses. | Atherosclerosis is a chronic inflammatory disease with high morbidity and mortality rates worldwide. Doublecortin-like kinase 1 (DCLK1), a microtubule-associated protein kinase, is involved in neurogenesis and human cancers. However, the role of DCLK1 in atherosclerosis remains undefined. In this study, we identified upregulated DCLK1 in macrophages in atherosclerotic lesions of ApoE |
36896610 | Addressing serious and continuing research noncompliance and integrity violations through action plans: Interviews with institutional officials. | Serious and continuing research pliance and integrity violations undermine the quality of research and trust in science. When researchers engage in these behaviors, institutional officials (IOs) often develop corrective action plans. Ideally, such plans address the root causes so pliance or research integrity violations discontinue. The aim of this study was to identify what IOs perceive as causes and action plan activities typically prescribed. We conducted semi-structured in-depth interviews with 47 IOs at research institutions across the U.S. including: institutional review board and institutional animal care and mittee chairs and directors, chief research officers, pliance and integrity officers, and institutional conflicts of interest chairs and directors. The mon root causes identified were: 1) lack of knowledge or training, 2) failure to provide research team supervision, and 3) researcher attitudes pliance. The mon action plan activities include: 1) retraining pliance or research integrity, 2) follow-up and hands-on involvement with the researcher, and 3) mandated oversight or mentoring. Because the monly identified action plan activities fail to adequately address the majority of root causes, our findings suggest a need for IOs to rethink existing approaches to action plan development to more effectively target root causes. |
36896611 | SIRT3-dependent delactylation of cyclin E2 prevents hepatocellular carcinoma growth. | Lysine lactylation (Kla) is a recently discovered histone mark derived from metabolic lactate. The NAD |