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FatigueNet: A hybrid graph neural network and transformer framework for real-time multimodal fatigue detection
Forest soundscapes improve mood, restoration and cognition, but not physiological stress or immunity, relative to industrial soundscapes
Unmet health information needs and associated factors among lactating mothers in Gedeo zone, Southern Ethiopia
Nanoparticles of non-porphyrinic covalent organic frameworks as contrast agents for photoacoustic imaging
Abstract Photoacoustic imaging (PAI) is an emerging biomedical modality offering non-invasive, high-resolution imaging with molecular contrast. Its effectiveness for molecular imaging is improved by using external contrast agents such as dyes and nanostructures, which enable targeted imaging, deeper tissue penetration, and enhanced diagnostic accuracy. However, the clinical translation of these agents has been hindered by limitations in biocompatibility, biodegradability, and functionalization versatility. In this study, two porphyrin-free nanoscale covalent organic frameworks (nCOFs), TAPB-PDA and LZU-1, were evaluated as novel exogenous contrast agents for PAI. Their photoacoustic responses were systematically measured and compared with traditional contrast agents, such as gold nanoparticles (AuNPs) and ATTO532-labelled mesoporous silica nanoparticles. Experiments at 532 nm across a range of concentrations demonstrated strong photoacoustic signals from TAPB-PDA and LZU-1, with enhanced response at higher concentrations. Besides, both nCOFs also provided detectable signals at the NIR window at the lowest concentration, indicating a broad working range. A nonlinear model of the volume-integrated photoacoustic pressure is also provided. Furthermore, in vitro cytotoxicity assays indicated excellent nCOF biocompatibility, even at higher concentrations. Photostability tests under prolonged pulsed laser irradiation revealed that both nCOFs exhibit good resistance to photobleaching and partial signal recovery under intermittent excitation. Collectively, these findings highlight the potential of TAPB-PDA and LZU-1 nCOFs as biocompatible, biodegradable, and functionally versatile contrast agents for advanced photoacoustic imaging.
Portability of short term wind power forecasting: investigating model calibration using wind power data from Ireland and UK
Abstract Wind power forecasting (WPF) models play an increasingly important role in integrating wind power into electricity systems. Portability of such models allows for quantified calibration features which can be taken from one farm and applied to another without compromising forecast accuracy. This paper investigates the portability of WPF methods by exploring the influence of model hyperparameter configurations on forecasting performance. The performance of two hybrid WPF methods are evaluated and compared, Variational Mode Decomposition & Feed Forward Neural Network (VMD-FFNN) and Ensemble Empirical Mode Decomposition & Feed Forward Neural Network (EEMD-FFNN). Supervisory Control and Data Acquisition (SCADA) data from wind farms in Ireland and the UK are utilised. The robustness and portability of the WPF methods when applied to different datasets are examined. The models demonstrated good forecasting accuracy, with the VMD-FFNN model achieving 3.42% NMAE error for the Irish site. For portability, the forecasting performance is found to be sensitive to two of the four model hyperparameters examined. A low number of modes used in signal decomposition, beyond a threshold of ~4 modes, is adequate for accurate prediction, although calibration is still required depending on the wind farm. Additionally, the number and variety of datasets improved model robustness.
Assessment of health economic losses caused by PM2.5 and ozone pollution in Beijing and Tianjin
Application of chemical similarity and bioisosteres to find allosteric inhibitors of type 2 lipid kinase γ
Lapachol interferes with the cell cycle and inhibits proliferation and migration of bladder tumor cells with effects on ncRNA expression
Behaviour and modelling of concrete incorporating agro-industrial wastes as a potential substitute for cement
New sampling strategy to estimate biomass yield and carbon sequestration potential of banana crops in India’s tropical semi-arid region
Optimizing root physiology and soil nutrient balance in rice–wheat sequence using polyhalite for enhanced profitability in the Indo-Gangetic plains of India
An efficient semantic segmentation method for road crack based on EGA-UNet
Growth and yield responses of sorghum (Sorghum bicolor [L.] Moench) varieties to sowing time in a rainforest zone of Nigeria
An effective image despeckling and reconstruction approach using U-Net based model and comparative analysis
Attention-enhanced hybrid U-Net for prostate cancer grading and explainability
Optimal attention deep learning based in-vehicle intrusion detection and classification model on CAN messages
Abstract Intrusion detection systems (IDS) have enormous significance to ensure the security of high-tech automobiles, especially those using the controller area network (CAN) bus for transmission between different electronic control units (ECUs). The CAN is a popular transmission protocol in automobiles, but it is vulnerable to a variety of attacks. To overcome these issues, several studies have investigated the use of IDS for the CAN bus. Researchers have been exploring safety issues of inter- and intra-vehicular transmission. In recent times, intrusion detection sensors are gained popularity despite how easily and effectively they can identify intrusion. Deep learning (DL) and machine learning (ML) algorithms have demonstrated their effectiveness for accurately and quickly identifying intrusions. However, DL approaches need vast quantities of data to accomplish superior outcomes which may be difficult in the case of CAN-based IDS. This manuscript presents an Optimal Attention Deep Learning based In-vehicle Intrusion Detection and Classification (OADL-IVIDC) model to secure CAN messages in model vehicles. The model begins with data preprocessing phase to effectively transform the input data into a more suitable format. For in-vehicle IDS, the OADL-IVIDC framework employs an attention-based augmented long short-term memory (A-LSTM) model. To further optimize the performance of the OADL-IVIDC system, hyperparameters are adjusted using the root mean square propagation (RMSProp) algorithm. The performance of the OADL-IVIDC system is assessed using a standardized car hacking dataset. Experimental results demonstrate that the OADL-IVIDC approach outperforms other techniques in various performance measures.
IFN-inducible human phospholipid scramblase 1 (PLSCR1) protein restricts HIV-1 infection by inhibiting membrane fusion
Human phospholipid scramblase 1 (PLSCR1) is an interferon-stimulated gene (ISG) that inhibits viral infections through various mechanisms. Here, we identify PLSCR1 as a host restriction factor that inhibits HIV-1 entry by impairing membrane fusion mediated by the envelope glycoprotein (Env). Using multiple cell types including the human SupT1 T cell line and purified CD4 + T cells, we demonstrate that PLSCR1 inhibits the replication of HIV-1 with diverse tropisms and subtypes, as well as HIV-2 and SIV. Mechanistically, we find that PLSCR1 blocks viral entry and cell-to-cell transmission, in part by restricting HIV-1 virion–cell and cell–cell fusion without affecting CD4 or CXCR4 expression or virus binding to the cell surface. Collectively, these findings establish PLSCR1 as a broad-spectrum lentiviral restriction factor that acts at the membrane fusion stage, thereby expanding our understanding of ISG-mediated antiviral defense.