Explore the vast range of titles available.

Potato stems and leaves biochar (PB) was prepared by pyrolysis at a temperature of 500°C under anoxic conditions. In order to strengthen the adsorption capacity, biochar was modified with alkaline solution (alkali modified biochar, APB). Two kinds of biochars were adopted as adsorbents to remove ciprofloxacin (CIP) from aqueous solution. The adsorption behavior of CIP onto biochar before and after alkali modified including adsorption kinetics and isotherms were investigated. The effects of different factors (equilibrium time, pH, temperature and initial concentration) during the adsorption process were also investigated. Biochar samples were characterized by Fourier transform infrared (FTIR), scanning electron microscopy (SEM), and nitrogen adsorption–desorption isotherm. The results showed that the alkali treated biochar possessed more mesopores than raw biochar, and accordingly exhibited a more excellent adsorption performance (23.36 mg·g−1) than raw biochar. Hydrophobic interaction, hydrogen-bonding interaction, electrostatic interaction, and π − π interaction were the adsorption mechanisms for CIP uptake onto the two adsorbents.

Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent drawbacks for consensus time synchronization due to inefficient frequent iterations. In this paper, a novel time synchronization algorithm for IWSNs with a mesh–star architecture is proposed, namely, fast and low-overhead time synchronization (FLTS). The proposed FLTS divides the synchronization phase into two layers: mesh layer and star layer. A few resourceful routing nodes in the upper mesh layer undertake the low-efficiency average iteration, and the massive low-power sensing nodes in the star layer synchronize with the mesh layer in a passive monitoring manner. Therefore, a faster convergence and lower communication overhead time synchronization is achieved. The theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm in comparison with the state-of-the-art algorithms, i.e., ATS, GTSP, and CCTS.

CSF visinin-like protein 1 (VILIP-1) levels have exhibited potential utility as a marker of neuronal damage and are increased in Alzheimer's disease (AD). The levels of CSF VILIP-1 have been associated with memory decline and hippocampal atrophy, while no studies have investigated the association between CSF VILIP-1 levels and cerebral glucose metabolism among older adults. Study participants had available baseline CSF VILIP-1 data and more than two assessments of 18-fluorodeoxyglucose positron emission tomography ([18F] FDG-PET) brain imaging. Linear mixed-effects models were used to examine the association between baseline CSF VILIP-1 levels and longitudinal changes in FDG-PET over time. Models were performed separately for the cognitively unimpaired (CU) and cognitively impaired (CI) participants. Among CU older adults, higher CSF VILIP-1 levels were marginally associated with a faster reduction in cerebral glucose metabolism. In CI older adults, CSF VILIP-1 levels were significantly associated with a faster reduction in cerebral glucose metabolism. These results provide novel insights into the relationship between neuronal injury and cerebral glucose metabolism, highlighting the potential of CSF VILIP-1 as a biomarker for monitoring and predicting the progression of neurodegenerative processes.

Schizophrenia (SCZ) is a severe mental disorder that impairs brain function and daily life, while its early and objective diagnosis remains a major clinical challenge due to the reliance on subjective assessments. This study aims to develop a machine learning-based framework for the auxiliary diagnosis of SCZ using multi-dimensional electroencephalogram (EEG) features and to investigate the underlying neural alterations. Resting-state EEG data were obtained from 45 male patients with pediatric SCZ and 39 age-and gender-matched healthy controls. Three types of EEG features (relative power (RP), fuzzy entropy (FuzEn), and functional connectivity (FC)) were extracted under various time window lengths and fed into four ensemble learning models. A data-driven feature selection approach (Recursive Feature Elimination) was applied to identify the most informative features, resulting in 212 most discriminative features (48 RP, 40 FuzEn, and 124 FC) out of the initial 760. Leveraging the selected features, the Categorical Boosting model achieved the highest classification accuracy of 99.60% at the 4-s window. Further analysis of the discriminative features revealed that the altered EEG characteristics were mainly in the alpha, beta, and gamma bands. Particularly, altered FCs exhibited a fronto-increase-parieto-decrease pattern mainly in the right hemisphere along with spectral-dependent RP alterations and a universally reduced FuzEn in the pediatric SCZ group. In summary, this study not only showcases the potential of advanced ensemble learning algorithms in precisely identifying pediatric SCZ, but also provides new insights into the altered brain functions in pediatric SCZ patients, which may benefit the future development of automatic diagnosis systems.

Spinal diseases and frozen shoulder are prevalent health problems in Asian populations. Early assessment and treatment are very important to prevent the disease from getting worse and reduce pain. In the field of computer vision, it is a challenging problem to assess the range of motion. In order to realize efficient, real-time and accurate assessment of the range of motion, an assessment system combining MediaPipe and YOLOv5 technologies was proposed in this study. On this basis, Convolutional Block Attention Module (CBAM) is introduced into the YOLOv5 target detection model, which can enhance the extraction of feature information, suppress background interference, and improve the generalization ability of the model. In order to meet the requirements of large-scale computing, a client/server (C/S) framework structure is adopted. The evaluation results can be obtained quickly after the client uploads the image data, providing a convenient and practical solution. In addition, a game of \"Picking Bayberries\" was developed as an auxiliary treatment method to provide patients with interesting rehabilitation training.

Time synchronization is one of the key technologies in Industrial Wireless Sensor Networks (IWSNs), and clustering is widely used in WSNs for data fusion and information collection to reduce redundant data and communication overhead. Considering IWSNs’ demand for low energy consumption, fast convergence, and robustness, this paper presents a novel Cluster-based Maximum consensus Time Synchronization (CMTS) method. It consists of two parts: intra-cluster time synchronization and inter-cluster time synchronization. Based on the theory of distributed consensus, the proposed method utilizes the maximum consensus approach to realize the intra-cluster time synchronization, and adjacent clusters exchange the time messages via overlapping nodes to synchronize with each other. A Revised-CMTS is further proposed to counteract the impact of bounded communication delays between two connected nodes, because the traditional stochastic models of the communication delays would distort in a dynamic environment. The simulation results show that our method reduces the communication overhead and improves the convergence rate in comparison to existing works, as well as adapting to the uncertain bounded communication delays.

BackgroundA better understanding of the molecular mechanisms that manifest in the immunosuppressive tumor microenvironment (TME) is crucial for developing more efficacious immunotherapies for hepatocellular carcinoma (HCC), which has a poor response to current immunotherapies. Regulatory T (Treg) cells are key mediators of HCC-associated immunosuppression. We investigated the selective mechanism exploited by HCC that lead to Treg cells expansion and to find more efficacious immunotherapies.MethodsWe used matched tumor tissues and blood samples from 150 patients with HCC to identify key factors of Treg cells expansion. We used mass cytometry (CyTOF) and orthotopic cancer mouse models to analyze overall immunological changes after growth differentiation factor 15 (GDF15) gene ablation in HCC. We used flow cytometry, coimmunoprecipitation, RNA sequencing, mass spectrum, chromatin immunoprecipitation and Gdf15–/–, OT-I and GFP transgenic mice to demonstrate the effects of GDF15 on Treg cells and related molecular mechanism. We used hybridoma technology to generate monoclonal antibody to block GDF15 and evaluate its effects on HCC-associated immunosuppression.ResultsGDF15 is positively associated with the elevation of Treg cell frequencies in patients wih HCC. Gene ablation of GDF15 in HCC can convert an immunosuppressive TME to an inflammatory state. GDF15 promotes the generation of peripherally derived inducible Treg (iTreg) cells and enhances the suppressive function of natural Treg (nTreg) cells by interacting with a previously unrecognized receptor CD48 on T cells and thus downregulates STUB1, an E3 ligase that mediates forkhead box P3 (FOXP3) protein degradation. GDF15 neutralizing antibody effectively eradicates HCC and augments the antitumor immunity in mouse.ConclusionsOur results reveal the generation and function enhancement of Treg cells induced by GDF15 is a new mechanism for HCC-related immunosuppression. CD48 is the first discovered receptor of GDF15 in the immune system which provide the possibility to solve the molecular mechanism of the immunomodulatory function of GDF15. The therapeutic GDF15 blockade achieves HCC clearance without obvious adverse events.

To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into the state space. The reward function simultaneously considers two objectives: reducing vehicle waiting times and minimizing carbon emissions, with the vehicle queue length as a weighted factor. Additionally, an ACmix module, which integrates self-attention mechanisms and convolutional layers, is introduced to enhance the model’s feature extraction and information representation capabilities, improving computational efficiency. The model is tested using an actual intersection as the study object, with a signal intersection simulation built in SUMO. The proposed approach is compared with traditional Webster signal timing, actuated signal timing, and control strategies based on DQN and D3QN models. The results show that the proposed strategy, through real-time signal timing adjustments, reduces the average vehicle waiting time by approximately 27.58% and the average CO2 emissions by about 7.34% compared with the actuated signal timing method. A comparison with DQN and D3QN models further demonstrates the superiority of the proposed model, achieving a 15% reduction in average waiting time and a 6.5% reduction in CO2 emissions. The model’s applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. This study aims to provide a flexible signal control strategy to enhance intersection vehicle flow and reduce carbon emissions. It offers a reference for the development of green, intelligent transportation systems and holds practical significance for promoting urban carbon reduction efforts.

This study aims to provide a non-contact detection of liquid composition inside containers using Microwave Radar Cross-section (RCS) measurement technology. Firstly, it analyzes the limitations of traditional near-infrared spectroscopy methods and proposes the necessity of introducing microwave detection methods. The research demonstrates through experiments a significant correlation between polar substances like total acid content and radar scattering capability, showing microwave radar’s effectiveness in reflecting the polarity characteristics of liquids. Furthermore, theoretical derivations and experimental validations illustrate that differences in electromagnetic properties of different liquid components lead to variations in echo loss, thereby impacting RCS levels. Experimental results indicate that microwave radar RCS measurement technology achieves an accuracy level of 2%, capable of distinguishing between different concentrations of ethanol, acetic acid, and other solutions. This study highlights the significant advantages of microwave radar RCS measurement technology in non-contact detection of liquid composition, providing new methods and a technological foundation for precise liquid component detection.

Oestrogen receptor alpha (ERα) is a well-known target of endocrine therapy for ERα-positive breast cancer. ERα-negative cells, which are enriched during endocrine therapy, are associated with metastatic relapse. Here we determine that loss of ERα in the invasive front and in lymph node metastasis in human breast cancer is significantly correlated with lymphatic metastasis. Using in vivo and in vitro experiments, we demonstrate that ERα inhibits breast cancer metastasis. Furthermore, we find that ERα is a novel regulator of vinculin expression in breast cancer. Notably, ERα suppresses the amoeboid-like movement of breast cancer cells by upregulating vinculin in 3D matrix, which in turn promotes cell–cell and cell–matrix adhesion and inhibits the formation of amoeboid-like protrusions. A positive association between ERα and vinculin expression is found in human breast cancer tissues. The results show that ERα inhibits breast cancer metastasis and suggest that ERα suppresses cell amoeboid-like movement by upregulating vinculin. Estrogen receptor alpha (ERα)-negative cells, which are enriched during endocrine therapy, are associated with metastatic relapse of breast cancer. Here the authors show that ERα inhibits breast cancer metastasis and suggest that ERα suppresses the amoeboid-like migration of breast cancer cells by upregulating vinculin.